1_GSLB_well_reach_distance.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/BYU-Hydroinformatics/gwbf-notebooks/blob/main/1_GSLB_well_reach_distance.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8mHySBw8PbMi"
      },
      "source": [
        "This scripts reads reads the following three sets of input:\n",
        "\n",
        "1. An **aquifer** file in GEOJSON or shapefile format\n",
        "2. A CSV file of **well locations**\n",
        "3. A CSV file of **water level measurements** (time series file)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLC55BdBCaKl"
      },
      "source": [
        "# 1.Install Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AyApLXOWCSDZ"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Installing packages**\n",
        "%%capture\n",
        "!pip install rioxarray\n",
        "!pip install gstools\n",
        "!pip install netCDF4\n",
        "!pip install xlsxwriter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "04I7TAGFCkIP"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Importing packages**\n",
        "import calendar\n",
        "import copy\n",
        "import datetime\n",
        "import math\n",
        "import os\n",
        "import shutil\n",
        "import tempfile\n",
        "import time\n",
        "import urllib\n",
        "from pathlib import Path\n",
        "from timeit import default_timer as timer\n",
        "from urllib import request\n",
        "from xml.etree import cElementTree as ET\n",
        "from datetime import date\n",
        "\n",
        "import geopandas as gpd\n",
        "import netCDF4\n",
        "import gstools as gs\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import requests\n",
        "import xarray\n",
        "import rioxarray\n",
        "from scipy import interpolate\n",
        "from shapely import wkt\n",
        "from shapely.geometry import mapping\n",
        "from google.colab import files\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import re\n",
        "import ipywidgets as widgets\n",
        "import plotly.express as px\n",
        "import xlsxwriter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J-jSUf4DkOTC"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Initializing Summary Logging**\n",
        "# Initialize summary string to record analysis inputs, parameters, etc\n",
        "summary = \"SUMMARY - Groundwater Level Mapping Analysis\\n\"\n",
        "summary += f\"Date: {datetime.datetime.now().strftime('%Y-%m-%d')}\\n\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bhGS6o2bEhG6"
      },
      "source": [
        "# 2.Uploading Aquifer Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vYAvS0-rPh6D"
      },
      "outputs": [],
      "source": [
        "#@markdown ### **Defining functions for this section**\n",
        "\n",
        "# looks through columns in dataframe to find matches to use as default for widgets\n",
        "def find_default_column(possible_names, columns):\n",
        "    lower_columns = [column.lower() for column in columns]\n",
        "    for name in possible_names:\n",
        "        if name.lower() in lower_columns:\n",
        "            return columns[lower_columns.index(name.lower())]\n",
        "    return columns[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nM0KXyk-kNyL"
      },
      "source": [
        "## 2.1 Uploading Aquifer File"
      ]
    },
    {
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              "\n",
              "     <input type=\"file\" id=\"files-b7bb80a2-abab-4fd4-9d67-84bf64b0fae8\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-b7bb80a2-abab-4fd4-9d67-84bf64b0fae8\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
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              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
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              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
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              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
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              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
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              "\n",
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              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving gsl_basin.dbf to gsl_basin.dbf\n",
            "Saving gsl_basin.prj to gsl_basin.prj\n",
            "Saving gsl_basin.shp to gsl_basin.shp\n",
            "Saving gsl_basin.shx to gsl_basin.shx\n",
            "\n",
            "Please select the appropriate headers for your file\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GridBox(children=(Dropdown(description='Aquifer ID', options=('OBJECTID_1', 'OBJECTID_2', 'NAME', 'BASIN', 'sq…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9b60b13686d34bb3b1fe51d5ef06ae99"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "upload_aquifers = files.upload()\n",
        "aquifers = gpd.GeoDataFrame.from_file(list(upload_aquifers.keys())[0])\n",
        "\n",
        "aList1 = list(aquifers.columns)\n",
        "\n",
        "default_a_ID = find_default_column(['AquiferID', 'Aquifer_ID'], aList1)\n",
        "default_a_name = find_default_column(['Aquifer_Name', \"AquiferName\", \"Name\"], aList1)\n",
        "\n",
        "a_ID = widgets.Dropdown(options=aList1, value=default_a_ID, description = \"Aquifer ID\")\n",
        "a_name = widgets.Dropdown(options=aList1, value=default_a_name, description = \"Aquifer Name\")\n",
        "\n",
        "a1Items = [a_ID, a_name]\n",
        "print(\"\\nPlease select the appropriate headers for your file\")\n",
        "widgets.GridBox(a1Items, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
      ]
    },
    {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Please select the aquifer you would like to map\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GridBox(children=(Dropdown(description='Aquifer Name', options=('GSL Basin',), value='GSL Basin'),), layout=La…"
            ],
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              "version_major": 2,
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            }
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          "metadata": {}
        }
      ],
      "source": [
        "#@markdown ### **Select the Aquifer you would like to Map**\n",
        "aList2 = list(aquifers[a_name.value])\n",
        "aquifer_name = widgets.Dropdown(options=aList2, description = \"Aquifer Name\")\n",
        "\n",
        "a2Items = [aquifer_name]\n",
        "print(\"\\nPlease select the aquifer you would like to map\")\n",
        "widgets.GridBox(a2Items, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
      ]
    },
    {
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          "data": {
            "text/plain": [
              "   OBJECTID_1  OBJECTID_2       NAME BASIN        sqmi     Acres  \\\n",
              "0           0           1  GSL Basin    01  787.035296  503705.0   \n",
              "\n",
              "                               AllReports  \\\n",
              "0  http://water.utah.gov/Landuse/publ.htm   \n",
              "\n",
              "                                FullRept99  \\\n",
              "0  http://water.utah.gov/Landuse/wrlui.pdf   \n",
              "\n",
              "                                          FullRept10 NonDigital  ...  \\\n",
              "0  http://water.utah.gov/Landuse/StatewideSummary...       None  ...   \n",
              "\n",
              "      Shape_Leng Gaz_ID HUC_6 HU_6_Name  TNMID     Shape_Le_1  Shape_Le_2  \\\n",
              "0  269759.984642      0  None      None   None  269759.984642    2.811615   \n",
              "\n",
              "  Shape_Area    Area_mi2                                           geometry  \n",
              "0   0.217221  787.548119  MULTIPOLYGON (((-114.05234 38.05464, -114.0528...  \n",
              "\n",
              "[1 rows x 41 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OBJECTID_1</th>\n",
              "      <th>OBJECTID_2</th>\n",
              "      <th>NAME</th>\n",
              "      <th>BASIN</th>\n",
              "      <th>sqmi</th>\n",
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              "      <th>HU_6_Name</th>\n",
              "      <th>TNMID</th>\n",
              "      <th>Shape_Le_1</th>\n",
              "      <th>Shape_Le_2</th>\n",
              "      <th>Shape_Area</th>\n",
              "      <th>Area_mi2</th>\n",
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              "      <th>0</th>\n",
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              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>01</td>\n",
              "      <td>787.035296</td>\n",
              "      <td>503705.0</td>\n",
              "      <td>http://water.utah.gov/Landuse/publ.htm</td>\n",
              "      <td>http://water.utah.gov/Landuse/wrlui.pdf</td>\n",
              "      <td>http://water.utah.gov/Landuse/StatewideSummary...</td>\n",
              "      <td>None</td>\n",
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              "      <td>269759.984642</td>\n",
              "      <td>0</td>\n",
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              "      <td>None</td>\n",
              "      <td>269759.984642</td>\n",
              "      <td>2.811615</td>\n",
              "      <td>0.217221</td>\n",
              "      <td>787.548119</td>\n",
              "      <td>MULTIPOLYGON (((-114.05234 38.05464, -114.0528...</td>\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-df50db58-ce3a-420b-aa26-5eb3a10242f1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-df50db58-ce3a-420b-aa26-5eb3a10242f1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_e49e1029-55df-4e1e-b4b0-2740c80cd25f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('aquifer')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e49e1029-55df-4e1e-b4b0-2740c80cd25f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('aquifer');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "aquifer"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#@markdown ### **Filter and Display Aquifer Information**\n",
        "summary += f\"aquifer file name: {list(upload_aquifers.keys())[0]}\\n\"\n",
        "summary += f\"selected aquifer: {aquifer_name.value}\\n\"\n",
        "aquifer_index = aquifers[aquifers[a_name.value]==aquifer_name.value].index.values\n",
        "aquifer = aquifers[aquifers[a_name.value].isin([aquifer_name.value])]\n",
        "aquifer_ID = list(aquifer[a_ID.value])\n",
        "aquifer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ke9EuCJnkTaq"
      },
      "source": [
        "## 2.2 Uploading wells file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502,
          "referenced_widgets": [
            "0117829b4fe543e5bd560462bada2110",
            "5b66df7f956e4d188a47944d8d4c6126",
            "0a45459841ef45dd83789f6be2464dd3",
            "0797205148644f0a852b8d5c5b358b38",
            "ae9d4c2e8a5c4233897219ea3396a8ff",
            "d96ce289fe1b4c73bea02e981782d883",
            "7665751f0ac84badb286a072013b8eb3",
            "6102d7f6b56b4b03abdab3beaff6ea8e",
            "95f1a5d7754a4ae494173d9207d3de85",
            "31b14da389ab45468ce6a24df551bfa6",
            "e3ec7761949845278debb26391a8f6a6",
            "8d09d82ff4e8428fb4eb90b102160cb6",
            "bf070d1aa7434592816b75b771b7f712",
            "3900bca4ec7941d491d6c46c8558c765",
            "ac5c1f1a4edc48a096b09223588291bb",
            "dd5bd4ba66f846f08fbf6c86a1c49ce0",
            "4b5b726e4a2f470d9795c03907219dfa"
          ]
        },
        "id": "LtMFJ3UFDY8T",
        "outputId": "602d4c4f-d620-48de-b440-268fbaed5b92"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-02de17ff-fd4d-4c13-ac83-94bd3dafb855\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-02de17ff-fd4d-4c13-ac83-94bd3dafb855\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving GSLB_1900-2023_wells_with_aquifers.csv to GSLB_1900-2023_wells_with_aquifers.csv\n",
            "           Well_ID           Well_Name    lat_dec    long_dec     GSE  \\\n",
            "0  381033113480701   (C-30-18)25aad- 1  38.175796 -113.802750  7098.0   \n",
            "1  381037113474001   (C-30-17)30bab- 1  38.176306 -113.795500  7193.0   \n",
            "2  381152113442801   (C-30-17)15cab- 1  38.197833 -113.741167  6550.0   \n",
            "3  381236113485601   (C-30-18)12cdb- 1  38.210028 -113.815500  7190.0   \n",
            "4  382113113435401   (C-28-17)22dda- 1  38.353571 -113.732473  5775.0   \n",
            "\n",
            "   AquiferID Aquifer_Name State  \n",
            "0          1    GSL Basin    UT  \n",
            "1          1    GSL Basin    UT  \n",
            "2          1    GSL Basin    UT  \n",
            "3          1    GSL Basin    UT  \n",
            "4          1    GSL Basin    UT  \n",
            "\n",
            "Please select the appropriate headers for your file\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GridBox(children=(Dropdown(description='Well ID', options=('Well_ID', 'Well_Name', 'lat_dec', 'long_dec', 'GSE…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0117829b4fe543e5bd560462bada2110"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "upload_wells = files.upload()\n",
        "wells = pd.read_csv(list(upload_wells.keys())[0])\n",
        "print(wells.head())\n",
        "wList = list(wells.columns)\n",
        "wList.append('NA')\n",
        "\n",
        "# find defaults for widgets\n",
        "default_w_ID = find_default_column(['WellID', 'Well_ID'], wList)\n",
        "default_w_name = find_default_column(['WellName', 'Well_Name'], wList)\n",
        "default_w_lat = find_default_column(['Latitude', 'Lat', 'lat_dec'], wList)\n",
        "default_w_long = find_default_column(['Longitude', 'long', 'long_dec'], wList)\n",
        "default_w_aquifer_ID = find_default_column(['AquiferID', 'Aquifer_ID'], wList)\n",
        "\n",
        "w_well_ID = widgets.Dropdown(options=wList, value=default_w_ID, description = \"Well ID\")\n",
        "w_well_name = widgets.Dropdown(options=wList, value=default_w_name, description = \"Well Name\")\n",
        "w_lat = widgets.Dropdown(options=wList, value=default_w_lat, description = \"Latitude\")\n",
        "w_long = widgets.Dropdown(options=wList, value=default_w_long, description = \"Longitude\")\n",
        "w_aquifer_ID = widgets.Dropdown(options=wList, value=default_w_aquifer_ID, description = \"Aquifer ID\")\n",
        "\n",
        "wItems = [w_well_ID, w_well_name, w_lat, w_long, w_aquifer_ID]\n",
        "print(\"\\nPlease select the appropriate headers for your file\")\n",
        "widgets.GridBox(wItems, layout=widgets.Layout(grid_template_columns=\"repeat(1, 550px)\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 632
        },
        "id": "mWepWYSG7ynf",
        "outputId": "d80831d3-fbd6-4130-f5f1-55fb9273d07f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           Well_ID           Well_Name    lat_dec    long_dec     GSE  \\\n",
            "0  381033113480701   (C-30-18)25aad- 1  38.175796 -113.802750  7098.0   \n",
            "1  381037113474001   (C-30-17)30bab- 1  38.176306 -113.795500  7193.0   \n",
            "2  381152113442801   (C-30-17)15cab- 1  38.197833 -113.741167  6550.0   \n",
            "3  381236113485601   (C-30-18)12cdb- 1  38.210028 -113.815500  7190.0   \n",
            "4  382113113435401   (C-28-17)22dda- 1  38.353571 -113.732473  5775.0   \n",
            "\n",
            "  AquiferID Aquifer_Name State                     geometry  \n",
            "0         1    GSL Basin    UT   POINT (-113.80275 38.1758)  \n",
            "1         1    GSL Basin    UT   POINT (-113.7955 38.17631)  \n",
            "2         1    GSL Basin    UT  POINT (-113.74117 38.19783)  \n",
            "3         1    GSL Basin    UT   POINT (-113.8155 38.21003)  \n",
            "4         1    GSL Basin    UT  POINT (-113.73247 38.35357)  \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Well_ID           Well_Name    lat_dec    long_dec     GSE  \\\n",
              "0  381033113480701   (C-30-18)25aad- 1  38.175796 -113.802750  7098.0   \n",
              "1  381037113474001   (C-30-17)30bab- 1  38.176306 -113.795500  7193.0   \n",
              "2  381152113442801   (C-30-17)15cab- 1  38.197833 -113.741167  6550.0   \n",
              "3  381236113485601   (C-30-18)12cdb- 1  38.210028 -113.815500  7190.0   \n",
              "4  382113113435401   (C-28-17)22dda- 1  38.353571 -113.732473  5775.0   \n",
              "\n",
              "  AquiferID Aquifer_Name State                     geometry  \n",
              "0         1    GSL Basin    UT   POINT (-113.80275 38.1758)  \n",
              "1         1    GSL Basin    UT   POINT (-113.7955 38.17631)  \n",
              "2         1    GSL Basin    UT  POINT (-113.74117 38.19783)  \n",
              "3         1    GSL Basin    UT   POINT (-113.8155 38.21003)  \n",
              "4         1    GSL Basin    UT  POINT (-113.73247 38.35357)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-495927d4-01c2-43cf-9872-9259d5fa024d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Well_ID</th>\n",
              "      <th>Well_Name</th>\n",
              "      <th>lat_dec</th>\n",
              "      <th>long_dec</th>\n",
              "      <th>GSE</th>\n",
              "      <th>AquiferID</th>\n",
              "      <th>Aquifer_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>381033113480701</td>\n",
              "      <td>(C-30-18)25aad- 1</td>\n",
              "      <td>38.175796</td>\n",
              "      <td>-113.802750</td>\n",
              "      <td>7098.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.80275 38.1758)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>381037113474001</td>\n",
              "      <td>(C-30-17)30bab- 1</td>\n",
              "      <td>38.176306</td>\n",
              "      <td>-113.795500</td>\n",
              "      <td>7193.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.7955 38.17631)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>381152113442801</td>\n",
              "      <td>(C-30-17)15cab- 1</td>\n",
              "      <td>38.197833</td>\n",
              "      <td>-113.741167</td>\n",
              "      <td>6550.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.74117 38.19783)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>381236113485601</td>\n",
              "      <td>(C-30-18)12cdb- 1</td>\n",
              "      <td>38.210028</td>\n",
              "      <td>-113.815500</td>\n",
              "      <td>7190.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.8155 38.21003)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>382113113435401</td>\n",
              "      <td>(C-28-17)22dda- 1</td>\n",
              "      <td>38.353571</td>\n",
              "      <td>-113.732473</td>\n",
              "      <td>5775.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.73247 38.35357)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
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              "        document.querySelector('#df-495927d4-01c2-43cf-9872-9259d5fa024d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-495927d4-01c2-43cf-9872-9259d5fa024d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5fe4e4cb-4136-4e9c-ad04-fc61525beb73 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wells_gdf",
              "summary": "{\n  \"name\": \"wells_gdf\",\n  \"rows\": 8752,\n  \"fields\": [\n    {\n      \"column\": \"Well_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7405800186036,\n        \"min\": 381033113480701,\n        \"max\": 424652111383301,\n        \"num_unique_values\": 8752,\n        \"samples\": [\n          402035112253101,\n          413848111533001,\n          404215111550001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Well_Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8752,\n        \"samples\": [\n          \" (C- 5- 5)32adb- 1\",\n          \" (B-11- 1)36dad- 1\",\n          \" (C- 1- 1)26dba- 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat_dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7499023111140831,\n        \"min\": 38.17579566,\n        \"max\": 42.7810333,\n        \"num_unique_values\": 8186,\n        \"samples\": [\n          40.37725,\n          40.6316144,\n          40.543565\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long_dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6980286878290362,\n        \"min\": -114.8255969,\n        \"max\": -110.609061,\n        \"num_unique_values\": 7881,\n        \"samples\": [\n          -111.6890914,\n          -111.8682668,\n          -112.2591333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648.6981969818967,\n        \"min\": 4080.0,\n        \"max\": 8800.0,\n        \"num_unique_values\": 2562,\n        \"samples\": [\n          5035.14,\n          4230.21,\n          4388.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AquiferID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Aquifer_Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"GSL Basin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ID\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geometry\",\n      \"properties\": {\n        \"dtype\": \"geometry\",\n        \"num_unique_values\": 8458,\n        \"samples\": [\n          \"POINT (-111.8893784 40.38772807)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "### **Formatting wells dataframe**\n",
        "#To avoid type mismatch, convert the aquifer_id found from the aquifer file and the aquiferid column\n",
        "#in the wells dataframe to both be strings\n",
        "summary += f\"well file name: {list(upload_wells.keys())[0]}\\n\"\n",
        "aquifer_ID = [str(item) for item in aquifer_ID]\n",
        "if (w_aquifer_ID.value !=\"NA\"):\n",
        "  wells[w_aquifer_ID.value] = wells[w_aquifer_ID.value].astype(str)\n",
        "\n",
        "wells_gdf = gpd.GeoDataFrame(wells, geometry=gpd.points_from_xy(wells['long_dec'], wells['lat_dec']))\n",
        "wells_gdf.crs = 'EPSG:4326'\n",
        "if (w_aquifer_ID.value !=\"NA\"):\n",
        "  wells_gdf = wells_gdf[wells_gdf[w_aquifer_ID.value].isin(aquifer_ID)]\n",
        "  print(wells_gdf.head())\n",
        "wells_gdf.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YJ1vIXj4-5aw"
      },
      "outputs": [],
      "source": [
        "wells_gdf = wells_gdf[wells_gdf[w_aquifer_ID.value].isin(aquifer_ID)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3HOoLzVJTLV",
        "outputId": "0aeb05bb-b93b-4306-b663-05d9a28b1368"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   OBJECTID_1  OBJECTID_2       NAME BASIN        sqmi     Acres  \\\n",
            "0           0           1  GSL Basin    01  787.035296  503705.0   \n",
            "\n",
            "                               AllReports  \\\n",
            "0  http://water.utah.gov/Landuse/publ.htm   \n",
            "\n",
            "                                FullRept99  \\\n",
            "0  http://water.utah.gov/Landuse/wrlui.pdf   \n",
            "\n",
            "                                          FullRept10 NonDigital  ...  \\\n",
            "0  http://water.utah.gov/Landuse/StatewideSummary...       None  ...   \n",
            "\n",
            "      Shape_Leng Gaz_ID HUC_6 HU_6_Name  TNMID     Shape_Le_1  Shape_Le_2  \\\n",
            "0  269759.984642      0  None      None   None  269759.984642    2.811615   \n",
            "\n",
            "  Shape_Area    Area_mi2                                           geometry  \n",
            "0   0.217221  787.548119  MULTIPOLYGON (((-114.05234 38.05464, -114.0528...  \n",
            "\n",
            "[1 rows x 41 columns]\n"
          ]
        }
      ],
      "source": [
        "import geopandas as gpd\n",
        "\n",
        "# Read a shapefile\n",
        "try:\n",
        "    gdf = gpd.read_file(\"gsl_basin.shp\")\n",
        "    print(gdf)  # Print the loaded GeoDataFrame\n",
        "except Exception as e:\n",
        "    print(f\"Error loading shapefile: {e}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "MGDfk069Dxo7",
        "outputId": "da77cf10-225d-493d-eabe-2774fe7207d6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "#@markdown ### **Plotting aquifer and wells**\n",
        "fig, ax = plt.subplots()\n",
        "aquifer.plot(color=\"none\", edgecolor=\"red\", ax=ax)\n",
        "wells_gdf.plot(ax=ax)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "wells_gdf.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "bwviwlW459KP",
        "outputId": "ce13cf9f-ee4e-4363-9ad5-962d69792184"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Well_ID           Well_Name    lat_dec    long_dec     GSE  \\\n",
              "0  381033113480701   (C-30-18)25aad- 1  38.175796 -113.802750  7098.0   \n",
              "1  381037113474001   (C-30-17)30bab- 1  38.176306 -113.795500  7193.0   \n",
              "2  381152113442801   (C-30-17)15cab- 1  38.197833 -113.741167  6550.0   \n",
              "3  381236113485601   (C-30-18)12cdb- 1  38.210028 -113.815500  7190.0   \n",
              "4  382113113435401   (C-28-17)22dda- 1  38.353571 -113.732473  5775.0   \n",
              "\n",
              "  AquiferID Aquifer_Name State                     geometry  \n",
              "0         1    GSL Basin    UT   POINT (-113.80275 38.1758)  \n",
              "1         1    GSL Basin    UT   POINT (-113.7955 38.17631)  \n",
              "2         1    GSL Basin    UT  POINT (-113.74117 38.19783)  \n",
              "3         1    GSL Basin    UT   POINT (-113.8155 38.21003)  \n",
              "4         1    GSL Basin    UT  POINT (-113.73247 38.35357)  "
            ],
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              "      <th>Well_ID</th>\n",
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              "      <th>long_dec</th>\n",
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              "      <td>7098.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>7193.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.7955 38.17631)</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>381152113442801</td>\n",
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              "      <td>38.197833</td>\n",
              "      <td>-113.741167</td>\n",
              "      <td>6550.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.74117 38.19783)</td>\n",
              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>381236113485601</td>\n",
              "      <td>(C-30-18)12cdb- 1</td>\n",
              "      <td>38.210028</td>\n",
              "      <td>-113.815500</td>\n",
              "      <td>7190.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.8155 38.21003)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>382113113435401</td>\n",
              "      <td>(C-28-17)22dda- 1</td>\n",
              "      <td>38.353571</td>\n",
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              "      <td>5775.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
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              "      <td>POINT (-113.73247 38.35357)</td>\n",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-02e258dd-bd3c-4951-a197-f0caaceada9f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-02e258dd-bd3c-4951-a197-f0caaceada9f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-04440d8c-37a7-4041-b1be-5ef532b8eb4c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-04440d8c-37a7-4041-b1be-5ef532b8eb4c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-04440d8c-37a7-4041-b1be-5ef532b8eb4c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wells_gdf",
              "summary": "{\n  \"name\": \"wells_gdf\",\n  \"rows\": 8752,\n  \"fields\": [\n    {\n      \"column\": \"Well_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7405800186036,\n        \"min\": 381033113480701,\n        \"max\": 424652111383301,\n        \"num_unique_values\": 8752,\n        \"samples\": [\n          402035112253101,\n          413848111533001,\n          404215111550001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Well_Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8752,\n        \"samples\": [\n          \" (C- 5- 5)32adb- 1\",\n          \" (B-11- 1)36dad- 1\",\n          \" (C- 1- 1)26dba- 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat_dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7499023111140831,\n        \"min\": 38.17579566,\n        \"max\": 42.7810333,\n        \"num_unique_values\": 8186,\n        \"samples\": [\n          40.37725,\n          40.6316144,\n          40.543565\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long_dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6980286878290362,\n        \"min\": -114.8255969,\n        \"max\": -110.609061,\n        \"num_unique_values\": 7881,\n        \"samples\": [\n          -111.6890914,\n          -111.8682668,\n          -112.2591333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648.6981969818967,\n        \"min\": 4080.0,\n        \"max\": 8800.0,\n        \"num_unique_values\": 2562,\n        \"samples\": [\n          5035.14,\n          4230.21,\n          4388.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AquiferID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Aquifer_Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"GSL Basin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ID\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geometry\",\n      \"properties\": {\n        \"dtype\": \"geometry\",\n        \"num_unique_values\": 8458,\n        \"samples\": [\n          \"POINT (-111.8893784 40.38772807)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OhbNlyiUoub2"
      },
      "source": [
        "# 3.Upload gages data (gages ID in GSLB)\n",
        "* the gages in GSLB is from Geoglows team\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "biBR0RMOtnTl",
        "outputId": "92f77fc0-6e20-4227-d4e5-836355e2c3b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Well_ID           Well_Name    lat_dec    long_dec     GSE  \\\n",
              "0  381033113480701   (C-30-18)25aad- 1  38.175796 -113.802750  7098.0   \n",
              "1  381037113474001   (C-30-17)30bab- 1  38.176306 -113.795500  7193.0   \n",
              "2  381152113442801   (C-30-17)15cab- 1  38.197833 -113.741167  6550.0   \n",
              "3  381236113485601   (C-30-18)12cdb- 1  38.210028 -113.815500  7190.0   \n",
              "4  382113113435401   (C-28-17)22dda- 1  38.353571 -113.732473  5775.0   \n",
              "\n",
              "  AquiferID Aquifer_Name State                     geometry  \n",
              "0         1    GSL Basin    UT   POINT (-113.80275 38.1758)  \n",
              "1         1    GSL Basin    UT   POINT (-113.7955 38.17631)  \n",
              "2         1    GSL Basin    UT  POINT (-113.74117 38.19783)  \n",
              "3         1    GSL Basin    UT   POINT (-113.8155 38.21003)  \n",
              "4         1    GSL Basin    UT  POINT (-113.73247 38.35357)  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2bef20bf-3cd9-42ca-a082-ed8118ec4fbb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Well_ID</th>\n",
              "      <th>Well_Name</th>\n",
              "      <th>lat_dec</th>\n",
              "      <th>long_dec</th>\n",
              "      <th>GSE</th>\n",
              "      <th>AquiferID</th>\n",
              "      <th>Aquifer_Name</th>\n",
              "      <th>State</th>\n",
              "      <th>geometry</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>381033113480701</td>\n",
              "      <td>(C-30-18)25aad- 1</td>\n",
              "      <td>38.175796</td>\n",
              "      <td>-113.802750</td>\n",
              "      <td>7098.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.80275 38.1758)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>381037113474001</td>\n",
              "      <td>(C-30-17)30bab- 1</td>\n",
              "      <td>38.176306</td>\n",
              "      <td>-113.795500</td>\n",
              "      <td>7193.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.7955 38.17631)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>381152113442801</td>\n",
              "      <td>(C-30-17)15cab- 1</td>\n",
              "      <td>38.197833</td>\n",
              "      <td>-113.741167</td>\n",
              "      <td>6550.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.74117 38.19783)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>381236113485601</td>\n",
              "      <td>(C-30-18)12cdb- 1</td>\n",
              "      <td>38.210028</td>\n",
              "      <td>-113.815500</td>\n",
              "      <td>7190.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.8155 38.21003)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>382113113435401</td>\n",
              "      <td>(C-28-17)22dda- 1</td>\n",
              "      <td>38.353571</td>\n",
              "      <td>-113.732473</td>\n",
              "      <td>5775.0</td>\n",
              "      <td>1</td>\n",
              "      <td>GSL Basin</td>\n",
              "      <td>UT</td>\n",
              "      <td>POINT (-113.73247 38.35357)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2bef20bf-3cd9-42ca-a082-ed8118ec4fbb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2bef20bf-3cd9-42ca-a082-ed8118ec4fbb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2bef20bf-3cd9-42ca-a082-ed8118ec4fbb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0477b246-9e32-4cc6-b3d0-4ea87286da18\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0477b246-9e32-4cc6-b3d0-4ea87286da18')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0477b246-9e32-4cc6-b3d0-4ea87286da18 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "wells_gdf",
              "summary": "{\n  \"name\": \"wells_gdf\",\n  \"rows\": 8752,\n  \"fields\": [\n    {\n      \"column\": \"Well_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7405800186036,\n        \"min\": 381033113480701,\n        \"max\": 424652111383301,\n        \"num_unique_values\": 8752,\n        \"samples\": [\n          402035112253101,\n          413848111533001,\n          404215111550001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Well_Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8752,\n        \"samples\": [\n          \" (C- 5- 5)32adb- 1\",\n          \" (B-11- 1)36dad- 1\",\n          \" (C- 1- 1)26dba- 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat_dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7499023111140831,\n        \"min\": 38.17579566,\n        \"max\": 42.7810333,\n        \"num_unique_values\": 8186,\n        \"samples\": [\n          40.37725,\n          40.6316144,\n          40.543565\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long_dec\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6980286878290362,\n        \"min\": -114.8255969,\n        \"max\": -110.609061,\n        \"num_unique_values\": 7881,\n        \"samples\": [\n          -111.6890914,\n          -111.8682668,\n          -112.2591333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"GSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 648.6981969818967,\n        \"min\": 4080.0,\n        \"max\": 8800.0,\n        \"num_unique_values\": 2562,\n        \"samples\": [\n          5035.14,\n          4230.21,\n          4388.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AquiferID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Aquifer_Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"GSL Basin\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"ID\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"geometry\",\n      \"properties\": {\n        \"dtype\": \"geometry\",\n        \"num_unique_values\": 8458,\n        \"samples\": [\n          \"POINT (-111.8893784 40.38772807)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "wells_gdf.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1Q8H0HhtxbG",
        "outputId": "6f07cb07-041c-4a4f-c4b8-26fccb894085"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'geopandas.geodataframe.GeoDataFrame'>\n"
          ]
        }
      ],
      "source": [
        "print(type(wells_gdf))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "quqbqf4JwQ2_"
      },
      "outputs": [],
      "source": [
        "# Read gage data from CSV file\n",
        "gages_df = pd.read_csv('gsl_nwm_gage.csv')\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "OHcdkEEXvpEi",
        "outputId": "4bf1f572-bc9c-499c-dde3-3e79b23f09dd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id                                               name  \\\n",
              "0  10011200  WEST FORK BEAR RIVER AT WHITNEY DAM - NR OAKLE...   \n",
              "1  10011500            BEAR RIVER NEAR UTAH-WYOMING STATE LINE   \n",
              "2  10015700  SULPHUR CR.AB.RES.BL.LA CHAPELLE CR.NR EVANSTO...   \n",
              "3  10015900       SULPHUR CREEK BL RES. - NEAR EVANSTON - WYO.   \n",
              "4  10016900                        BEAR RIVER AT EVANSTON - WY   \n",
              "\n",
              "                  River   latitude   longitude  elevation_m    state  \n",
              "0  WEST FORK BEAR RIVER  40.841614 -110.927119         2797     Utah  \n",
              "1            BEAR RIVER  40.965225 -110.853508         2432     Utah  \n",
              "2         SULPHUR CREEK  41.129114 -110.806563         2205  Wyoming  \n",
              "3         SULPHUR CREEK  41.156058 -110.835176         2173  Wyoming  \n",
              "4            BEAR RIVER  41.270224 -110.963793         2057  Wyoming  "
            ],
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              "        vertical-align: middle;\n",
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              "      <td>BEAR RIVER</td>\n",
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              "      <td>SULPHUR CR.AB.RES.BL.LA CHAPELLE CR.NR EVANSTO...</td>\n",
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              "      <td>SULPHUR CREEK</td>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-75f81c55-7342-4201-a601-37e810d6b4ca button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-75f81c55-7342-4201-a601-37e810d6b4ca');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ae70d0f3-01d0-47f0-8dda-12ebe15a1a53')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ae70d0f3-01d0-47f0-8dda-12ebe15a1a53 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gages_df",
              "summary": "{\n  \"name\": \"gages_df\",\n  \"rows\": 78,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49997,\n        \"min\": 10011200,\n        \"max\": 10172952,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          10129300,\n          10011200,\n          10129500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 78,\n        \"samples\": [\n          \"WEBER RIVER NEAR PEOA - UTAH\",\n          \"WEST FORK BEAR RIVER AT WHITNEY DAM - NR OAKLEY - UT\",\n          \"WEBER RIVER NEAR WANSHIP - UT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"River\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          \"PROVO RIVER BELOW DEER CREEK\",\n          \"FARMINGTON CREEK\",\n          \"CENTERVILLE CREEK\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7461186516982347,\n        \"min\": 39.459528,\n        \"max\": 42.64492,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          40.75106,\n          40.841614,\n          40.792726\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49708596765338847,\n        \"min\": -114.023722,\n        \"max\": -110.806563,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          -111.370472,\n          -110.927119,\n          -111.404918\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"elevation_m\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 289,\n        \"min\": 1286,\n        \"max\": 2797,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          2057,\n          1709,\n          1876\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Utah\",\n          \"Wyoming\",\n          \"Idaho\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "gages_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "PL5VKkzSmtgV",
        "outputId": "a9e0b90e-df65-48a7-afe3-47afdab20aec"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
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              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\n",
              "    If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              "&gt;&gt;&gt; ser = pd.Series([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=ser, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              "&gt;&gt;&gt; df1 = pd.DataFrame([1, 2, 3], index=[&quot;a&quot;, &quot;b&quot;, &quot;c&quot;], columns=[&quot;x&quot;])\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(data=df1, index=[&quot;a&quot;, &quot;c&quot;])\n",
              "&gt;&gt;&gt; df2\n",
              "   x\n",
              "a  1\n",
              "c  3</pre>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "type(gages_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSg9Hj2WD_XY"
      },
      "source": [
        "## 3.1 Plot wells and gages"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_wells_and_gages(wells_gdf, gages_df, aquifer, title=\"Wells and Gages Locations\"):\n",
        "    \"\"\"\n",
        "    Plot wells and gages locations with enhanced visual style\n",
        "\n",
        "    Parameters:\n",
        "    wells_gdf: GeoDataFrame containing wells information and geometry\n",
        "    gages_df: DataFrame containing gages information\n",
        "    aquifer: GeoDataFrame of aquifer boundary\n",
        "    title: Title for the plot\n",
        "\n",
        "    Returns:\n",
        "    fig, ax: matplotlib figure and axis objects\n",
        "    \"\"\"\n",
        "    # Set the style to 'seaborn-v0_8' to avoid deprecated styles\n",
        "    plt.style.use('seaborn-v0_8') # Changed line to use seaborn-v0_8\n",
        "\n",
        "    # Create figure and axis with white background\n",
        "    fig, ax = plt.subplots(figsize=(12, 8), facecolor='white')\n",
        "    ax.set_facecolor('#f0f0f0')  # Light gray background\n",
        "\n",
        "    # Plot aquifer boundary\n",
        "    aquifer.plot(\n",
        "        color=\"#e6f3ff\",  # Very light blue fill\n",
        "        edgecolor=\"#2171b5\",  # Strong blue edge\n",
        "        linewidth=1.5,\n",
        "        alpha=0.3,\n",
        "        ax=ax,\n",
        "        label=\"Aquifer Boundary\"\n",
        "    )\n",
        "\n",
        "    # Convert gages DataFrame to GeoDataFrame\n",
        "    gages_gdf = gpd.GeoDataFrame(\n",
        "        gages_df,\n",
        "        geometry=gpd.points_from_xy(gages_df.longitude, gages_df.latitude)\n",
        "    )\n",
        "\n",
        "    # Plot gages first (triangles)\n",
        "    gages_gdf.plot(\n",
        "        ax=ax,\n",
        "        color='#d95f02',  # Orange\n",
        "        marker='^',\n",
        "        markersize=80,\n",
        "        label='Gages',\n",
        "        alpha=0.8,\n",
        "        edgecolor='white',\n",
        "        linewidth=0.5\n",
        "    )\n",
        "\n",
        "    # Plot wells\n",
        "    wells_gdf.plot(\n",
        "        ax=ax,\n",
        "        color='#7570b3',  # Purple\n",
        "        marker='o',\n",
        "        markersize=40,\n",
        "        label='Wells',\n",
        "        alpha=0.8,\n",
        "        edgecolor='white',\n",
        "        linewidth=0.5\n",
        "    )\n",
        "    # Plot stream\n",
        "    streams = gpd.read_file(\"gsl_stream.shp\")\n",
        "    streams.plot(\n",
        "        ax=ax,\n",
        "        color='#64b5ed',  # Adjust color to a slightly darker blue\n",
        "        linewidth=0.5,\n",
        "        label='Streams',\n",
        "        zorder=0\n",
        "    )\n",
        "    # Customize the plot\n",
        "    ax.set_title(title, fontsize=14, pad=20, fontweight='bold')\n",
        "\n",
        "    # Customize legend (Corrected)\n",
        "    handles, labels = ax.get_legend_handles_labels()\n",
        "    legend = ax.legend(\n",
        "        handles, labels,  # Pass handles and labels explicitly\n",
        "        loc='upper right',\n",
        "        frameon=True,\n",
        "        fancybox=True,\n",
        "        shadow=True,\n",
        "        framealpha=0.9,\n",
        "        edgecolor='none',\n",
        "    )\n",
        "\n",
        "    # Add subtle gridlines\n",
        "    ax.grid(True, linestyle='--', alpha=0.3, color='gray')\n",
        "\n",
        "    # Set equal aspect ratio\n",
        "    ax.set_aspect('equal')\n",
        "\n",
        "    # Remove axis labels (if they're not needed)\n",
        "    # ax.set_xlabel('')\n",
        "    # ax.set_ylabel('')\n",
        "\n",
        "    # Add a border to the plot\n",
        "    for spine in ax.spines.values():\n",
        "        spine.set_edgecolor('#666666')\n",
        "        spine.set_linewidth(0.5)\n",
        "\n",
        "    # Adjust layout\n",
        "    plt.tight_layout()\n",
        "\n",
        "    return fig, ax"
      ],
      "metadata": {
        "id": "zxnwTi1bbJPC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plot_wells_and_gages(wells_gdf, gages_df, aquifer, title=\"Distribution of Wells and Stream Gages\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 880
        },
        "id": "DxBCzI72du0o",
        "outputId": "8108a50c-7adc-420a-d86e-e2d39db6081a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-27-a642ad9a9c08>:76: UserWarning: Legend does not support handles for PatchCollection instances.\n",
            "See: https://matplotlib.org/stable/tutorials/intermediate/legend_guide.html#implementing-a-custom-legend-handler\n",
            "  handles, labels = ax.get_legend_handles_labels()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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Z2edD8vMgO1QO9ApHGDp0aO7z9u3be+1bVVXV4++CgoLc5z3FY27dunW357TZbJSWlrJ9+3Zqamr22PY9ccIJJ+TijpcvX55LdCorK6OyspLi4mKsViuJRIIVK1b0KNmTFafdRcZdd92Vi2nuTlZk7wsHw/beffddHnnkEdavX09HR0ePmEQgd73d6c/9amhoyC3LCr0sQ4YMoampqX8XiTHKcOGFF7J+/XqWL1/O6tWr+eSTT3JCePHixXznO9/Zp6TBIUOG9Pg7GwYAhhd6wYIFvfbp7OykpaWF4uJitmzZwgMPPMAnn3xCa2trrxjNvvqtoKAAh8OR+zt7bwoKCnqEHmSX760Emdvtzn1ubW3NhSdkr6d7HHSW7l7l/t777r8hu977qqqqXrab7Utd13Nid1c2bNjAqaeeyrXXXssbb7zB1q1befDBB3nwwQexWq2MGzeOOXPm9HiZk0j2FylOJUcEyWSSNWvWAIYI2ZP3yel08thjj/Hmm2/yxhtvsHz5cmpqali9ejWrV6/mhRde4Iknnui393R/foj7ilXMPlg0rffXcteH0K4P1gNl1wdz97/7it3d1TOyr7GXfZ2z+7LdxQv3h4kTJ+JwOIhGo6xcuTLXV1OmTAHAYrEwYcIElixZwpo1a3LJMBUVFQwePLjX8UaNGoXP5+u1fH/KHR2o7b3zzjvcdtttZDIZzGYzxxxzDE6nk/r6+h5ibVf6c7+629iuw7J93av+MHr06B7ezvfee49vfetbhMNhlixZgt/v77Nv+2JP37PS0tI+7x0Y19LU1MTVV1+d8ywOHz6cgoICQqFQryTE7uyu33aNTe2v/Y8aNSrnpdy8eTOjRo3KrfvGN76RS2j8+OOPueaaa3rsuy/3ftckqv6iaVoPb3J3sgK8qKiIF198kVdffZV3332XFStWUF9fz9KlS1m6dCkLFizg0UcfxWKx9Pu8EsmuSHEqOSJ45plnciJjTwkJWVRVZebMmcycORMwEpSefPJJ7r33XrZt28Yrr7ySS6b5PMjGZWUffn6/n0AgANBjCDXr4ds1m7i713N/6e653Lx5c068Zf/ua7uDfc7uhMPhnMfnQM5pNpuZOnUq7777LitXrsw9qKdNm5bbZurUqSxZsoR169blhpdPPPHE3PrunsNrrrmmVxLPgXAgtvfMM8/kMvr//ve/5+ru/uxnPzvg0mXdk/BqampySWeZTKbXvdoda9as4YMPPmDLli1cf/31jBkzpsf6k046iRNOOIHXXnsNgFAo1G9xuivdwwamT5+eq4XbF//85z9zwvTrX/96TgS+9tprPSpcfN7MmjUrN0HGP//5T2bPnt3vF7F9ufe7u5fQ+3sHhr1v3boVIQR///vf9yosLRYL559/Pueffz5geIEfeOABHnvsMZYvX84HH3zAaaed1q/rkkj6QsacSg5rhBDMmzePX/7yl4Ah7PYW//mrX/2KWbNmcfvtt+e8Cj6fLycWsseFnl7MfUkK2Rt+v5+HH3449/cDDzyQ+9xdRGUfwEuWLKG5uTnXjt2VUuru0dlb3cjZs2fnhiYfeeSRXCJOOBzmL3/5C2B4hLIPoINBdXV1TrC89tprrF69GjD6+7777st57A70nNnh+WzRd+jZr9nP3etrdi9DdcIJJ+SGc5966qlcDHM4HOab3/wm//Vf/7Xbe7An9sX2+qK7hy4rODZv3sz8+fNzy3d9kekv3UtoPfzwwzlv6cMPP7zHJLfuNDU18bvf/Y4XX3yRH/7wh72+M5s2bcolHpWXl+fsu7vd9vd7VlBQkPPyvfnmm7kXNiEE//d//8fXv/71nGDt3m/ZkIZgMJhLvIP977d9YeTIkbmyWStWrOCOO+7odd7ly5f3SMTMsi/3PhsXDcbsallbe+mll/oMRznzzDMB40Wke0LmBx98wC233MIdd9yRC+/Jzmp1991357YrKirqkaS4JxuWSPqD9JxKDjtuvvlmTCYTmUyGmpqaXOmkyspK7r///r16YqZMmcKjjz7K9u3bOfXUUxk0aBDJZDInYoqKinIJFUVFRbjdbkKhEC+88AKbN2/m4osv7pE0sD+MGDGChx56iBdeeIFUKsWOHTsAo8RN96lNL7vsMu655x6i0Sjnn38+Y8aMYfXq1Zxwwgk9kj26H7f7vuXl5butPVhcXMzdd9/ND3/4Q7Zu3crMmTMZNmwYNTU1hEIhAL797W/38n4dCIqi8Mtf/pJrr70Wv9/PFVdcQVVVFR0dHTnxfeqppx6w1zorNLNiNy8vr0ffTJw4EbPZnIsRVBSlhzj1er3ceeed/PjHP2b16tWcdtppub7x+/3k5eXxrW99a5/btS+21xennHJKzut45ZVXUl5ezqZNm/jmN7/Jvffei67r3HbbbVx55ZWce+65+9S27P3ftm0b//nPfzjttNNwuVy0trZSXV3drxjbmTNncvnll/Pkk0+ydu1azjrrLKqqqnA6nXR2duYEpM1m42c/+1lOcHW/N9/4xjcYNmxYryl4++J//ud/uOqqqwiHw5x33nmMHDkSv99PfX09qqrmqlWccMIJmM1mUqkU99xzD/PmzWPbtm2ccsoppFIpPvvsM+677z5WrFjRr/MeCHfddRfRaJSFCxfy8ssvs2jRIkaNGoWmadTW1vao1nDmmWdyyimnAPt272+88UYeeOABtm/fzptvvsmpp55KXl4e27dv59hjj+1VMeHCCy9k/vz5LFmyhN/+9rc8++yzuFwu1q9fTyaT4YwzzsjF/E6aNImXX36ZJ554grfffpuysjKi0WhuhrWRI0f2GIWQSPYH6TmVHHYsW7aMJUuWsHz5cpLJJJMnT+bOO+9k/vz5/Zr7e+bMmTz66KPMmjULVVVZs2YNNTU1DBkyhGuuuYbnnnsu510xmUz84he/oLKyEk3TaGxsPKCpLbPDcsOHD+fRRx+lpKSEtrY2XC4XZ555Jk888UQPcX3NNddwxx13UFJSQjgcprm5me9973u94tGyXH755Zx11lk4HA4ikche480uvPBCHn/8cc4880xsNhvr169H0zROO+00Hn74YW699db9vtbdMXr0aJ5//nkuv/xyiouL2bx5M5FIhEmTJvGTn/yE+++/v1dM375SXV3dI+knm6GfxeFw5OqZgjGjV/fkFDDE/YMPPpgTrWvXrsVqtXLRRRfx9NNP73MpINg32+uLSy65hO985ztUVFQQi8VIpVL87Gc/45ZbbuGb3/wmLpcr92Kxr5hMJh5++GFmzpyJw+EgGAxSWFjIww8/3EM87o27776bhx56iDlz5lBWVsb27dv59NNPaWtr45hjjsmVYOouYM4880wuvfRS3G438XiccDjcZ+z1rhxzzDE8++yznHPOOXi9XjZs2EA4HOaUU07h4YcfZvbs2YAhfu+77z5Gjx6NyWSipaWFq666il/96ld861vfoqSkhGQy2WcZroONzWbjD3/4Aw899BBz587NJWtt2LABi8XCjBkz+NrXvsYrr7zCn/70p5w97Mu9t1gsOTtzOp1EIhHcbjcPPvhgrrJAd0+spmk89NBDfOMb36CqqoqmpiY2b95MVVUVd9xxB7///e9z23/lK1/hz3/+MyeffDKpVIpVq1bR2NjIyJEj+epXv8oTTzzRZ51aiWRfUIT0v0skEolEclRw/fXX8+GHH+Lz+XrNFieRDBSk51QikUgkkiOIuro6LrnkEr70pS/x/e9/PxcDum7dutzkE9OnTz+UTZRI9oj0nEokEolEcoRxyy238M477wBGmbS8vDw2bNhAKpXC4/Hw5JNP7lO4hkTyRSLFqUQikUgkRxjJZJInnniC+fPnU1dXRzQapbi4mBNPPJFbb721RykuiWSgIcWpRCKRSCQSiWTAIGNOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBilOJRKJRCKRSCQDBu1QN6A7//M//0NBQcGhboZEIpFIJBKJ5CDj9/v53//9371uN6DEaUFBAddff/0Xes5EIkFDQwPl5eVYrdYv9NySgYW0BUkWaQsSkHYg2Ym0hYPDY4891q/t5LA+IIQ41E2QDBCkLUiySFuQgLQDyU6kLXxxSHEqkUgkEolEIhkwSHEqkUgkEolEIhkwSHEqkUgkEolEIhkwHPXi1Gw2U1FRgdlsPtRNkRxipC1IskhbkIC0A8lOpC18sQyobP1DgaqqMvNOAkhbkOxE2oIEpB0cDggB6bTC55+rZEJRTKTTn/d5Dl8UBTRNoCgHfqyj3nOaTqdpa2sjLS3uqEfagiSLtAUJSDs4HMhkvghhaqDr+hdzosMUIYz7cTA46j2nmUyGYDCI2+1G04767jiqkbYgySJtQQLSDg4HuuvFTEYnk0l/biWfdF1HVY96n94eyWQyfPLJhzidDqZPn7bf35sD+rbdc889PProo2zYsAGAV199lfvvv5/a2lry8vKYO3cu3/72t+WXWiKRSCQSyedGJpMhmUwCB8dz1xuBEKLLe/p5nePwR1FUOjs7aWtro7Ozk1mzztovDbjfrwDr1q3jxRdfzP29Zs0a7rzzTr773e+yfPly/vrXv/L888/z+OOP7+8pJBKJRCKRSPaKEXohReNAwWQy4fcH2L59+37tv18uTV3Xueuuu7juuuv4/e9/D4Ddbue3v/0tp5xyCgCjRo1i8uTJbNq0qd/HFUKQSCR6LFNVFbPZjBCi662oJ9lg9WQy2cuVr2kaJpOJTCbTK2ao+3FTqVSv81osFhRFIZVK9YozMZlMaJrW53EVRcFisQD0OiYYGX+qqpJOp8lkMn0eV9d1UqnUbq/18zrunvqwr+Pu7d7sqQ/3dG/21of9uTd9Xevejps9Vl/xZYdbHx7IcQ+1fffVh3s67ufxG5FIJHJ/H459KH8jDs5vRCKR6PF8OJL68Mj5jbABu87e1NewvnKA64yYSiPZR/Sxfv+Pe3D3PfTHzWQMm1AUaGtrZ9iwYTn77i/7JU6ffPJJrFYr5557bk6cjhgxghEjRgCGe33JkiUsXbqUX//61/0+bjqdpr6+vscyl8tFcXFxn+sAhg8fDkBra2uvL0JRURFut5twOEx7e3uPdXa7nbKyMlRVJRaL0dzcjMlkyq0fMmQIJpOJ9vZ2otFoj30LCgrwer3EYjFaWlp6rLNYLFRWVgLQ0NDQ60eosrISi8VCZ2cnoVCoxzqfz0d+fj6JRILGxsYe60wmE0OGDAGgqamp15e6rKwMu91OMBjE7/f3WOd2uykqKuqzDxVFYdiwYQC0tLT0+pEqLi7G5XIRiUR69aHD4aC0tBRd1/u8N0OHDkVRFNra2ojFYj3WZfswGo3S2traY53VaqWiogKgz+MOGjQIs9lMZ2cn4XC4x7psH8bjcZqamnqsM5vNDBo0CIDGxsZeP7jFxcV4vV7C4TCRSKTHOo/HQ2FhIalUao992Nzc3OtHtaSkBKfTSSgUorOzs8c6p9NJSUkJmUymz2vNHre1tZV4PN5jXWFhIR6Ph0gkQltbW491NpuN8vJyoO8+HDx4MJqm0dHR0eta8/LyyMvLIx6P09zc3GNd9z7sy74rKiqwWq34/X6CwWCPdV6vl4KCApLJJA0NDT3WqarK0KFDgb77sLS0FIfD0ad9fx6/EZlMBl3XMZlMCCH6PK78jTA4kn8jUqlUj+dDeXk5NpuNQCBAIBDosa/8jTD4on8jysqMZdnTGOfrLayyorKveFSlK72873XG/sYq0Sv5as/7ZtfRq03Z4x5Ym/q61uy+h+64weDO3632duO7nf2N6C+K2MfI4ba2Ns4991z+9a9/YbPZOOOMM3IxpwAvvPACP/jBD7Bardx5551cdtll/T72vffey1VXXdVj2RfhOT3c3hqlV+Tz8ZweSX14ONv3QPCc9ue4A7kPj3T7lr8R/bvWI9W++/KcJhLxbsJx3z2GiqKQ79TpiCi73J+D4Ynse11dXS1PPPFPli9fSigUxOVyc8wxx3LVVddQVVXdj+P2tf7Qe05feeWF3NJhw4Yybdq0nH0/+eST3H777X3s35N99pz+4he/4KKLLqKqqoq6urpe6y+44ALOOeccVq5cyXe+8x2EEFx++eX9OraiKLutKbendUDuy9AXJpOph1e0O0IYbwEWi6XPLLw9Fdzd03GBPbZX07TdBgnvrbbe53XcPfXhno67t3vzefXhno67P32o6zrxeByLxXLE9OGhujcD0Q735TdC13WSySQmk2mv7R2IfXi43ZuB+huRtYNdnw9HUh8e7r8Rfehq9hx72vc6h5og+sk8HGMvIpLZ/T3a/bH37ZybN2/iO9/5BueeewH33/8QeXn5NDc38fTT/+a//utr/Pa39zF69DF7Oe6+n7d/6w7suCbTzvuqaZacjexLYtQ+idPFixezYsUKXn755T0fVNOYOnUqV155JY899li/xemhIJVK0dDQkBtqkBy9SFuQZJG2IAFpB0cLiqKghXbQ8s7DFA+ZhuKs6mPo2nBk7RyOPzD+9Kd7mT79OG666au5ZaWlZXzzm/+PysrKnJBbunQJ//jHX6mrq8XhcDJnzjlce+2NuX0ef/xRnn32STRN44orrubjjxdz7LHjuOaaG9B1nX/962HeeOM1OjraGTJkGLfd9g3Gjh0PwKJFr/Dvf/+L9vY2PB4vF1xwCZdeOjD02j6J0/nz59Pe3s5pp50G7IxZmDFjBjfccAObNm3iN7/5TW57RVFkGSmJRCKRSCQDFoeaIPjO3wEIvvMQjnPu3ov39MDo7Oxk7drV3Hvvn/tcf9FFXwYgFovxk5/8iNtu+yazZ5/N9u1b+eY3b2PUqNEcf/yJvP/+uzzxxD/55S9/R1XVKB544D42bdrAsceOA2DevKd5663Xueee31BSUsqCBS/y4x/fyeOPP0c4HOJPf7qXP/7xAYYNG8HGjRu4887/x6RJk6mqGvW5XXt/2adSUnfeeSeLFi3ixRdf5MUXX+Rvf/sbAC+++CLTpk1j4cKFLFy4kHQ6zaZNm3jyySdzQlYikUgkEolkIJH1msZrlgMQr1mOFqrNJfh8HjQ2GsleFRWD9rid3W7n3/+ex6xZc7uS6kYwfPgINm5cD8CSJYuZOnU648ZNwG63c8stX+sR57tw4QIuvvgyKiuNBMELLrgEl8vNxx9/SDQaQdd1bDY7AKNGVfPssy8NCGEK++g59Xq9eL3e3N/ZQOjS0lJKS0v53e9+x7333ssdd9xBYWEhZ599NrfddtvBbbFEIpFIJBLJQaC71zTL5+09zere7glhq1at5M47v9P1l6CoqJhHH32Sd955k+eee5rm5iZ0XZBOpxg3bgIAHR3tlJdX5o7hdLqorNwpeBsaGrj//j/wl7/cl1um6xlaW1s45ZTTmTlzFjfe+BXGjZvI1KnTmTVrDh7PTo13KDmgMffKysoemfpnnXUWZ5111gE36otGTkcmySJtQZJF2oIEpB0cyezqNc0Sr1mOJ1S7m9jTA6eycjCKorBjx3YKCwsBGD9+Iq+88gZgxIL+618Ps3z5Uv7wh9/xgx/8mBNPPBlN0/j2t3dmuuu6QNN6JqQpyk57tVot/L//9z1OOunUPtvx7W//N1/+8pV8+OF7vPvuWzz99OP88Y9/pays/CBf8b5z1H/rrFYrQ4cOlcHuEmkLkhzSFiQg7eBIpy+vaZbgOw/hULuXwlK6hN+BD/e73W4mT57Ks88+1ef6rCDesGEdgwYN4pRTTkfTNJLJBDt21OS28/l8PerMRiIR6up25P4uK6tg69YtPY7d1GTUR9Z1nXA4REVFJZdeegX33fdXhgwZxvvvv3vA13cwOOrFqUQikUgkkqOL3XlNs3zesae33/4tNmxYx89/fldOMAaDQV555SX+/ve/Mnr0MZSUlNHa2kpLSzOdnR388Y+/o6CggLY2Y1KKSZOmsGTJR6xf/xmJRIIHH/wLVqstd45zzjmP+fOf57PP1pLJZHjnnTe5+eZraGlp5u233+Qb37iV2lpDzDY3N9Ha2pqb3OJQc9Sn0ieTSZqbmykpKdlj/TnJkY+0BUkWaQsSkHZwJLMnr2mWnrGn2VJSCgfDezpo0GD+/OcHeeyxR/j2t28nGAxgt9sZObKa2277Jqeeejq6rrN48XvcdNPVeL0+brnla0ybNoPf/OaXPPjgX7jxxlvZuHE9d9zxX7jdHq655ga2bNmYE9SzZ59DS0sLd9/9Q6LRCIMGDeauu35OcXEJp51WTE3NNu64478Ih0Pk5eUzZ845nHDCSQd8bQeDo16cCiFIpVKfS1yJ5PBC2oIki7QFCUg7OFLZm9c0y+cde1paWsZ3v/v93a5XVZUf/vDuXstPOeX03Oebb/4at9/+rdzf//rXwxQWFuX2v+66m7juupt6HUNRFK6//mauv/7mA7iCzw85rC+RSCQSieSooT9e0yy9Y08HDqtWreSii+ayfv1nZDIZFi16hc7ODiZNmnKom3bAHPWeU4lEIpFIJEcPDquKMuVCnFMu7Nf2dqtKJPo5N2o/GD9+ItdffzM///n/4vd3Ulpazo9+dDelpWWHumkHjBSnEolEIpFIjhraYhqUzOj39pGYAAZmaMfFF1/GxRdfdqibcdA56sWp2WymtLQUs9l8qJsiOcRIW5BkkbYgAWkHRypG+Oi+ik2lq3j+5zdzlGQnR704VVUVh8NxqJshGQBIW5BkkbYgAWkHkl2RwvSL4qhPiEqn03R0dOSmYpUcvUhbkGSRtiCBw9MOUhmdWDIjKwx8Dsg+/eI46j2nmUwGv9+P0+lE04767jiqkbYgySJtQQKHnx2EE2lW7giR1gVWTaHYbaXYY8FtG/htH/h0jzuVHtTPm6PecyqRSCQSyeFOKqOzYkeQtC7QUYmnFWo74yyrCbKjI3aomyeR7BNSnEokEolEcpjTFk6R0UGgMrzYicOqIboe8ZoqPX2SwwspTiUSiUQiOcxxWU2AMfDsdlqpiWooCBSg1Gs9pG2TfH7885//4BvfuBWARYte4ctfPv8Qt+jgcNSLU1VVcblcqOpR3xVHPdIWJFmkLUjg8LKDllAy99msKqQVEwIFAXREUoeuYUcUB+6B/trXbuLBB+/vsWzTpg2ceeZJvPfeOz2WP//8s1x22flHZSLWwP/Gfc6YzWaKi4tlHTuJtAVJDmkLEji87KAzkgIUfA4z8TQUe2woRmFOAtHDp9rAocJkUjGbTZhMu5NFSld/HphAnTZtBsuWLe2xbNmypdjtdlas6Ll8+fKlTJkyPXcfjyaOenEqhCCVSh2VbyaSnkhbkGSRtiCBw8MOOqMpatpjRBIZBAoWTaXArqBpJo6p9OK1W6jtjNMZTbG2IcTymgCpjH6omz1g0DQVVTXT3BRj3Zp2mptiqKoZTetLHh24HUybNoOtWzfj93fmlq1YsZQzz5zD8uU7xWkmk2bVqpVMmzaDLVs2c8cd/8UFF8zhkkvO4c9//sNey5vpus4DD/yJyy47n3PPPZNbb72OTz75+IDb/0Vx1IvTZDJJbW0tyWRy7xtLjmikLUiySFuQwMC3g03NET6tDbGtLYZAwWXTGOKx4W+LU9TSzJYVjbgTgklleTQGkrSGUgTjGQIx6UkFQ5iGgzrPPbWKN17bxLKldbzx2iaee2oV4aC+i0AVXS8pByZQx4w5BofDyfLlywDDxtauXc2FF15CW1srLS3NAKxfv454PMa4cRP5wQ++w+TJU3nmmfncd9/f+PTT5Tz99BN7PM/bb7/BihVL+dvfHuWFFxZy4YWX8utf//ywqdl71ItTiUQikUgON/zRFPX+hJGRr5gozXMw1GmlvibAIw9+wgvPreHN/2zmmX9/ytOPryRPaAwudGEzm2gP9xTbiqKgqiqKohBNZuiIJI8K76qum3h1wTqSyUyP5clkhldfWYeumw76OU0mjcmTp7B8+ScArFmzioKCIiorBzFmzLE57+ny5UsZNWo0a9euRgi44oqrMZvNlJWVc+mlV/D666/t8TzhcBiTyYTVasNkMjF79tk89dQLh0W9XpBF+CUSiUQiOazI6IJ1jWFAwWnTmDzERyqWoW6HnwXz1xGP7/SO6bqgrTXCK/M/45LLJxBJgM9nI5zQAUEqAc1NYTo7Ynh9NjwFDpqicSKJNCOLHZT7bIfsOj9PTCaV5qZwL2GaJZnI0NIcpqTUTuYgC/WpU2fw2GOPAMaQ/qRJUwCYNGkKy5cvZfbss1mxYhnTps2gsbEev7+TuXPP6HYEsdc46FNPPYP//GchV1xxIVOmTGfGjOM57bSZh0VyH0hxKpFIJBLJYcXG5giJtECgUuq1oSjQ1Boh4I/3EKZZdF0QjaZobgrRsL2T99/czHnnH4vFamLevDXEEmlURUFBwWxROWvuaLAq1LTHjlhxqqoK/r1MTuDvjFNW7iDTt37db6ZNm8G99/6aHTtqWLlyOZdccgUAEydO4YUXniMWi7Fu3Vpuuuk21q//jCFDhvHgg4/u0zk8Hg/33fdX1q5dzUcffcA///kPXnrpBe6990+YTANf+h0eEloikUgkEglpXdAWTgIKBW4r5T4jKz8cTtHZGd/9fmmdjo4Y48aX4m+P8erL62hpCiNSOk6zhiZApDKIZIZ3/7ORCpedRFocscP7ui7w5dv3uI0vz4auH/xkuKKiYoYOHcaSJYvZvHkTEydOAqC6uppEIs6iRa9gtzsYPXoM5eXlNDU1EItFc/sHgwGi0ejuDg9AMpkgHo9z7LHjuPHGr/K3vz3Ktm1b2bJly0G/ns+Do16cWq1Whg8fjtUqixQf7UhbkGSRtiCBgWkH6xrCXTNBKZR1FdcXAvI9Fnx5u/dymkwqeXl2otEkefl2otEUoWACIQTJZIZgIEGsa1lLU4RQW5RSn51E+sgUp5mMTnGJC4ul77hSi9VEcYmr25C+gqKoHIxap2AM7c+f/zxDhgzD6/UBRjzquHETeP75Z5gyZSqqqjJ16gy8Xh9//ev9RCIROjra+elPf8xDD/1lj8e///4/8utf/4xAwI8Qgk2bNiCETnFx8UFp/+fNUS9OJRKJRCI5HBBC0BFJIVDJd1nIc1pyy/MKHHg9NqzW3kO2igJ2h0ZxsYu21igFhU4yaZ1AII7FoqFnjPjTdFpH10HPCOp2+Klw2NBTKspBEmQDDVXNMOfsMb0EqsVqYs7cMajqQR7P74YRT9rAxImTeyyfNGkKDQ31TJ06AwBN07j77l+wY8d2LrvsfL761RuoqKjklltu3+Pxb7zxq6iqieuuu4LzzpvF/ff/kR/84C58vrzP7ZoOJooYQAXcfv/733P99dd/oedMJpO0trZSVFSExWL5Qs8tGVhIW5BkkbYggYFpBx9t9RNPCexWM1OG+nLL6yLgiETpaI3y8vx1JBJG7KmiQHGxk9nnjCEWTRIOJfl0ZQOhYIJpMwbzycc7yGQEyWSaVMrwEppMCqedUUVRsYt1G5qZMW0QRcVWxEGo83mwSSYN4RyPx9kfNaNpKrpuoqU5jL8zji/PRnGJC1XNkO7hNTZKSR2MQvxHOosWvZT7PHz4cKZO3SnAH3vsMW6/fc/CGmRCFEIIEonEgC6yLPlikLYgySJtQQID0w7KfVa2tsaIJtJ8sKmdQfl2Bhc4qHRCwmQjndK59saptDSHCAUTFBQ6ycu38+F72xleVYBJU+nsiOHxWHG5LQSDCdwea4/YSotVw+4ws21bB8ccU8KCl9dx2RUTsO05RPOwxBCgOiWldsrKHei6IJNJoR+Z0QyHDUe9OJVIJBKJ5HChzGulPZwiEEsjdKhpixKKpxlV6sJuU4lZTLzx2iacTgtWm8aG9S20tUaZPbcaq03jrTe24HBYOOnU4Wiais2mYVIVMhlDnNpsGrPmVPPx4hrGjS8jHEqASaW5OczQYe4BJdQPJpmMftCz8iX7jxSnEolEIpEcJphNKpMGewgn0jQHk9R2xOkIJ1m8uYOhhQ6GFjk494JjqN0RoL09wpRpgygpdaMogo3r25gwsYwhw/KZ//xa8vNtXH39VOrr/LQ0R/B6bdgdZlYur8fvj+NwWmhtDWPRTDQ1hqmo8GA2MyCH9yVHFlKcSiQSiURymOGyariKNKyayuaWKAqGFxVgaIGDomIXDfUBdvg7ef+dbThdFtweK9OmD2Lb1nbGjS+joNCOEDrLPqlHUWDj+iShUIKiYidnzhrFpyvqGVVdRHtbhBFVBTz19GrOOKOKslKbFKiSz5WjXpxqmkZRUdFhM6WX5PND2oIki7QFCRwedlCZZ6PcZ2VVbQh/LE1NWxSvw0xBkY3pxw3mszXNaCYTpWVuhgzJo6kpiFkz4cuzk07rLF9ax/TjBhGNpAiF4gwa7APgvXe2oShgtWkkkzpOl5XW9ijPv7iWyy6fQL734E/tObBRUBTjf8nnz8D9xn1BmEwm3G73oW6GZAAgbUGSRdqCBA4fO1AVhQmD3GxpjWFTNJp3BGkHhA6fLKnF4TBTXxegsMjJe+9ux+WyoOuC444fzMhRRbS1RrHZTBQWF7Dkox1s39pBcYmLk04ZzidLdjD37GpWr27EpKokEjoNDSFcLg8W09FWjVIK0y+Ko16cZjIZwuEwLpcLk+loexOUdEfagiSLtAUJDGw7UBQFRVEQwihxpCoqbmHiuXlrSKd0xo8twaQo+P1xAv44ui6IhBOccOIQPnhvO60tYbZt6WDSlHJOPnU4yaROY0OAEVUFzDhuME6XhY62CNOmD2btmiaC/gQOs4lQIo3fH2dtvQmrplCZZ8NjP1qkhEAK1C+Go8Widks6naa9vR2bzTbgfnwkXyzSFiRZpC1IYGDagYJCLCZobgrR0RGjMN9KcZkXgFdeXo+aAVWHtuYwx4wtRegCRTUE1dtvbuW0M0Ywc9ZIEvE0tTv8uN02tmzuoLkpiMWmUV1dREGBg38/thIhBBldkNIFugCLCmaTgs9nI5SGQCxFSyjJiVU+zEe8FzVb5xSkQP38OerFqUQikUgkhwMKCq0tCRa+sp5kMgMCMrrA5bIydcYgErE0wYBRl7VVgcJCJzabRipl1EhqaQ7z1htbjLJSXisTJpXj98fpaIuQX2Bsq2kqwWCczs4YFquJuGJCATQFdAU8DguDB/tYWudHQcGkCFRFijXJweVIf9WRSCQSieSIIBYTvPLyekKhJMmkTjKl09oSJRpN0dYaIeCPk05nUFUFXcB772zly1dMwGLRMHV5Nluawzzz5Kd4PFY8bis2mwmHw0Jenp1Bg3ysW9fMG69tZvbZ1djsFlwaeMwKDk3BbtWYffZoWqJJjHmSdKyaikmV4lRycJGeU4lEIpFIBjiqolJX56epKYyuC/Lz7bS1RhACVFXB47Gh6wJVNWJR02md9etaAfjKdZPp7IjR3hYhv9BBSbGbxoYQGV2Q57MRDifYsL6FzZvacXusJBNpTj19BJddMZ7WljD+zhi+PDslpW6sdhP+1kiuXRV5tkPVJYc16XSaxx9/lLfffoO2tlZAobp6NNdeeyPjxk1g06YNhEIhJk+eeqibekg46sWpqqrY7XZUVTqRj3akLUiySFuQwMCwg2ziUzIJO7b7EbrAYjGRSum52Zp0XeB2W7BaNZLJNCAwmQxv5vp1rdTU+Ln2hqmsX9dCKJzAbDbxzptbaWmJUFTsZMbxgxk2vIBBQ/LQNBWz2URLS5hoNMXQUfkMEUbMqRACHRhc4KCmPYamKNR1xqnwWbvmnJf0lwceuI81a1bz4x//lMGDh5JIJHj++We5887/x0MP/YuFC1/BbrdLcXq0YjabKSsrO9TNkAwApC1IskhbkMAXawfds+8VRUFVVcLhNM2NIcLhJA6HBY/XhhACTVNJp3fOtRmNJPD748w+u5qFCzaQSKRxuVWjTqlV48xZo+hoj7BpQxuJRJod2/2cdMowAv44fn+ceCzF8KpC3ntnK3U7/ASDCabNGERZhYIO1AR0XGYIp6AxCuGUoBMXJYSJp3Te39RJqdeKx66R5zBj0Y7ElzrloArw5cuXMmvWXIYNGwGAw+HgqquuobS0lH//+18sXLgARVF57723efTRJ/nKVy5lzpxzWbjwZaZOnc5//dd32bJlMw88cB+bNm1E00ycdtqZ3Hrr7bm6vM899xQvvjiPzs5OioqKuf76mznppFMA+PWvf47NZkfXM7z55n/wen3ceef/sHr1pzz33FMA3HTTbcyaNReAJ598nJdeep5AwE9RUTFXXXUtM2fOOmj9sStHvTjNluHI/jBIjl6kLUiySFuQwOdvB4qioCoqkWiG5sYQ8XiawmIXsUiSTEbwn0UbEQLyC+14PHZKy9xYrUaCk81mzh0nGEygqgrr1jYxe241wWACRQWv10YqpfPRhzWoKsyaW81rCzeSTKZ59+2tpFIZvF4bQ4fl8fyzq2lpDnc1zNjX6bTwSZNOICHwWBTMCjg0GO1T8FbaaPDDluYwGSGo9yep9ycAqCp2UCmH+/dIZeVgXnvtVaZMmU5V1cjc8jPOOIszzjiL+vo6xow5lptu+mpu3dtvv84vf/k7yssriMfj/OAH3+GCCy7hnnt+Q1tbK3fd9X2efvoJrrzyGlatWsnf//43/vznBxk6dBivvfYqv/zlTxg37ll8vjwA3nnnTe644/t87Wvf5H//90f8/Of/y9y55/LEE/N4+ukn+Mtf7uPMM2ezbt1ann/+Ge67768UFRWzbNkn3H33j5gyZTp5eXmfS/8c9eI0mUxSX19PRUUFVqv1UDdHcgiRtiDJIm1BAp+fHWTLQXV2RkklBf9ZtIG8PDtjji3lkQc/4czZo3jjtU3EYikURcFu1xgyJJ+lH9cya67hHTWb1Zyn1WbTsNk0jh1bxrvvbCWRyDBkqI/SUhfpdJKJk8vp7IghhOCa66fQ0R4lHEqAAppm4t23t+4UpoDNquFyWykodFKgZXAXmdD7uI5ynw2HxUQ4kaYjnMQfTaEi2NxiTKN6qATqGr/CmsAX91I51isY69u36Vy//vVv8fOf/y+33XYDJSWljB07nunTj+dLXzoZi8XS5z7Tps2goqISgI8/XowQcMUVVwNQVlbOpZdewb///RhXXnkNY8eO5+mnX8DlMiaROP30M/nNb37B9u3bmDjREJQVFZUcd9yJAEydOo1PP13OZZddhdlsZsaME3jkkYfw+zsJh8OoqorVaoRvTJ06nRdfXPi5hrsc9eJUIpFIJJIviu7loKbNGMKrC9aTSKQ54UvDeOWldVgsJqKRFNFoCpNJIZMR7NgRYOqMwQT8cVavbGTOOdWk0zrxeAah61htZt56YwsgOO2MEaAo1Nb4CQQSlJS6aWkO4fMZntD2tghrVjVxwklDSSbTvPnaZtq6JTjZbEZGfmGRk8WLa2htDDH77NEUFNv7FKg+hxmfw0xlnp1IIs2auiDJtE5Ne4yMLsh3mnHbvlipMda372Jx7+z0oh+MOqfFxSX84Q9/oaZmG8uXL2PVqpXce++vePTRh/jtb+/bzT6luc+NjfX4/Z3MnXtGjzaazYZHXdd1HnvsEd599238fn9ui1QqlftcVFSc+2w2W/B6fbn9swI5mUwyadIUqqpG8pWvXMqkSVOZNm0GM2fOwm63H2g37BYpTiUSiUQi+QJQUAhHBC8+vxbVZNQTTcTTeL02wqEE8XgaX56dQCAGQFe+E0IXrFxWZwzLv7qBzrdiOOwaY44p5pixpdTVBTjm2BIKCh2UlXvI82gMGuylvjbIqpUNFJe6yC9w8vFHO2isD3LWnGrefWsLoVCSmbNGoes6bS0RXG4LxSVuzGaV9z+uZcfWTqwmhYUL1nPJ5eMx2/c8EYHTqjFteB4fb+kklcmwrS3G9rYY04d5sVsGxiQGA40hQ4YxZMgwLrzwEjo62vn612/h+eef6XPb7hNBWCxWhgwZxoMPPtrnto899gjvvPMWP/3pLxk+vAohBLNnn9pjG3WXEmC784RaLBZ++tNfsWXLZhYvfp/58+fx7LNPcv/9D+F0uvbhavvPkRi1LJFIJBLJgEJBoaM9xYZ1LbS3RVEVhY72KOm0jsttwe83BGk0ksTrtXffEYDmpjCrVjZw1pxqTj19BMeOLyO/0EksksRhUxg/Lp9hvk7Sz91Cpnktqqpgs2lUjSxEVRTaWiNUVRUwc9Yo1qxupLY2QDAQJxpJ4PXa0DSF4hIX7765hWefWsW6jZ3ogECQTGZobgr1K+5WVRSOrXCjqCoCFQF8vC1ATXvsgPovmdaJJTN73/AwoLW1hT/+8bdEo9Eey/PzCxgxoopYLL7XY5SXl9PU1EAstvMYwWAgd8z16z/j+OO/RFXVKFRVZfPmjfvd3nQ6TSQSYcSIKr7ylet44IF/AArLly/d72PuDSlOJRKJRCL5nInFBB98sJ2Ojhi6gGAwnhOh/kAcn8/4HAwmcDjNWK1dA5td3lNNU2luDvPBu1sxmVScTjPbt7bjcwmKtz5C+ulrCT1xE+m2rQTfeRC7lsDpspBOG4Pxbo+VYSMKcDrNjKgqYPbc0Vx59STKyz0s/mA7ZeVeXnhuLfX1QVJpgapAJAVdOU74O2P0t9a+x25mxvA8PHYzOoZI3dYWY2trdO8790E4kebjbX4+3hZgR8eBidyBgM+Xx7JlS/nlL39Kbe0OdF0nHo/z5puvs2LFMo4//kSsVitNTY2EQiF0IRAYnnS9y50+deoMvF4ff/3r/UQiETo62vnpT3/MQw/9BYDS0jK2bt1MPB6npmYbTz31OE6nq6um6r7xzDP/5oc/vIPW1hYAduyoIRQKUlZWcdD6ZFeO+mF9i8XCkCFDZD1DibQFSQ5pCxI4eHagA/X1Idpao4wdV4qe0QkGdorQQGccV1ed0kQizceLdzBrbjWvL9pINJpCUUAzm9AEnDFrFCuW1eP22GioC7G1No/yiTeSP/oURKIrdlQBM2k8eW6sNo32tgixaIpYLIkvz47ZoqKqKrqA+voAxx0/FL8/SktzGK/PhtB17GaNaFqgYHhPfXl29H0I49RMKhMGe9GF4NMdAcLxNDs64risJorcln2qflDbESejGxcWiqX3oecPJgevYoPZbOa3v/0j//znP7jzzv9HIOBHVVVGjBjJD35wF9OmzSAajfC73/2K6667gn8/9UJuXyFAR6BpGnff/Qv+/Offc9ll5+NwODnhhC9xyy23A0ai1M9+dheXXHIOQ4cO57vf/T6FhcX8+c+/x+v17VN7L774MlpamrntthtIJBIUFZVw001f7VFl4GCjiGwV3wHA73//e66//vpD3QyJRCKRSPYbvatovaYqpHVBe3uK1SsaWbG0nnPOP4YF89eRSKQpLnFx3AlDWPTKBgoKHcw4fgivvLSOZDLNsBH5jB1bmsvYt1hNlJZ5aG4K8c5bW5hx3GCWLa2namQBY44poaDIsduYUKNkFZhUhaA/wY5tfoLhBC6nFZNJ4f13tzHj+MH8Z+FGnC4LGQFp1YRDE/gTUOAyccUVE/Yac7onPt7SQSqdAUSuLcOLHJR591wFoa4zzuaWKKJroFdF54QqH+au6ViTSUMwxuNxBo6aObjoQuy8NuNtAUUxQigGIosWvZT7PHz4cKZOnZz7+7HHHuP222/f6zGOerdAKpWiqampRwab5OhE2oIki7QFCeyfHUSTGRZv8fPh5k5W14Vo7kjx+mub8HltKKrC8q7EJqtVo6U5zEcf1nDWnFGMG1+G0HWuv3kal14xgcFD8lBNKsOrConFUthsZhYuWG+0K5nB5bLi74zhdtvo6IjQ3hrBtBuxIoQgowtSGYGeEVitGpvWt/LW65uY94xR33TFsnq+fMUEzGYTZk3FpYGKgs1qYuac0TRkTHTE91/9HVvpwW41I1DJCJVkRqF2L0P0wVg6J0yzZxbAkm0Bkum+agd8noiuGbm+eAXc/bZ2adMjVohnOeqH9XVdJxqNfm6FZCWHD9IWJFmkLUhg/+ygOZgglREIVCIpgRKKEvInsNo0bFYT/o4YHy82BGk0kiIQ6DlD0+BhXoqLXVg0lZaWCFs2teH12fnowxomT61k5fI6zppTzepPG7FYTDicZhobQphMJtydCdw+S58ln8AQqTa7hsVmYtpxg/F3xAgE4kbBfbcFRYVLL59AZ0eEzs4Yvjw77kI3DWmVBr9OIg1zh+6ft85l1Zgy1EdaF3xWHyQYTRJLCvzRFD6Huc99Pms0aq8KYEZVPluaw7SHkqQyOktrAswY5uNglHXqP+ILPV82vrS7EFUUBV0XmBQj3EL5Qq//i+OoF6cSiUQi6T+6EKQz4gidovLgIYChRS7q1rehZ3Q+/riWM84axUcfbKelJcJLL3yGx2MlL9+em6EpGIjTPrkcu91MIqXj9dmIRpJYbSZOO6OKUCjOxMmVrF3TTHNLmLPmVPPRhzWMG1+GxWrig/e3MeFLVbhcKk6tb9GiWVTcbiuZtKC4xIXTZcZi0fD6bNhdFtxuM05fHoOVPHRhCFovsL5DML1k3/ogrQtSOtg0iKWhPQ6DXAqFbiuBaAoQrKwN4baZsJlVrJrxr8RjpTWUJJ7SEagMKXTwzGb4cpWbVCZIMJpC1wUDdFT7oJARooeTVlEMWaxATh8LwRHbB1KcSiQSiaRfdEZTrK0Pk9aFnKKyG7oQJNM6QpArOK8AHrtGUaGTTEbQ3hrhow9rOO+isbS1RqirDeB0WrA7zLz79laCgTizz67Gbjfz/rvbqB5TjMNhQTNr+Lw2nG4bqVSGzo4YEyeVk4in+ejDGoIBI5nKZjPTHs4QagtRkufb7eizDnjyrLlEKVW14HRbiWlW8l0mMgBCkOm2v0DgT0JdRKHMKfYS6yhoiQm2BaEhDBYTJHVI6zClyNiv3GfDpCrUd8aIxNOE4jqBmN5VDUCwpbWrzisqFfl2YqqdYrvAYlJwWjVC0SReuzZgYy4PFNFNmPYVW6ryRftwv3ikOJVIJBLJXgnF06yqCyGEgkBlc0sUi6ZQ7D56p3fN6IK6zjjb22I5Ldi93JKuC4pLXTgcGtFokuamEP9ZuIEvnTyMMccWE4umaG4OMeP4wRQVO7FZNd5+aws7dgSoHlPM8qV1TJpawUcf7SAaTnL8l4ZSVuHmnbe2UrOtE4vFxOyzR2O1mQgFYohUmngwhknJMzxvu0EHzHYTZYO9qAps8guWtuh8eTfRCwoK11TDe42w0Q+j9xDl8HEzxFLQFAOfFcqdCmYVUhloi8OWoE6xXWGkz8JkjzU39ek7DYLjigWdkRSJlFHP1GHVGFbo4LmtgsmFRsca6wRu28Ao6n+wh9YFIlcVQVXos0KAYOAmDGUyB6eawlEvTk0mEwUFBT1mXpAcnUhbkGSRttCbbMawQOny2ijUdcSPaHG6Nzto8MfZ1hYDlFy/BJM6brOK06ZhM5tQzXDuBcfy5GMriUaT1NUGeO/dbUydVonFYqKw0InHY8Ni1WhqCnLMsaVMnTYYRRGcesYIGhqCVFUVUlLqxuHQaGuNMnRoHhMmlpOXZycWT/Hhe9uYMm0Qqa5SUf0t+SSEYEWbYFmL4Msj9yywTKrCl8oE/9pgXGl1Xm95tNEvyOhwaqWxLpQUdCSMeqkuCxRpMASFpA72LvXhsmq4rCY8QcHIEmO/aDKDP5qi0GUBBaJpKHYY28dTRkRtNqxEVUH/QnKjlNwQuqDru5DrZyPEIDv0vj8+TdGtlmnujF0nTAvIRmno2eXZDP4B5j7t7Ow4KMc56sWppml4vd5D3QzJAEDagiSLtIWdRBJp2sKpPmfnCcUzxFMZbOYjU8TvzQ6SGUMd6CgML3LidWr8e00MJQlFJhvhBoHLDGOLHVx701S2bGyjvT1KYZETj9dOwB8FFCLRJMmkMXXp2lVNdHTESGd0pk6rpLMjSjqls2JZPZFwEpNZZdacaj5b28ybb2zGZjVx/IlDWb2qEYvZREmpG13o/fLmbQ7o7AjBdWOUfm2vqQoXDRe8uBVKHAKf1dhHR7CuQ7A1CHOH7DyO26Lgtuz1sNRHIJGGcErgMis4LCYcXdOdbvALhnkUXGYFXQiiyQwK5EpJmUwCIb4ohaYghOhT/HcXq4qyt9CHnmT6OGD33buLUCHoSobaWZR/oIQ3xOMxPvtsVe5vIfY/LvioF6eZTIZYLIbdbpdekqMcaQuSLNIWdrK+KUIonsHwDhoxgJ91CCyZOA6T4OOtAcp8VoYU2LEeYUlSe7KDBn+ccDwNXcO6LpuG22rmlinZzHNBJA3NUVi4XeeswRaOHV9KW2uU2lo//s4Y+QVOWprDJBJpyiu8hEIJ8goceLw2PF4b+QV23B4rgUAcp9OKw2mmtMxNa0sYk6pw5lkjQYHF721H0VROmzOGFSETo1SBby8ObV0IPmqCS6v6J0wBImnBOw1wcoXCuw2CqcXGsP32oHGdswcruy1ntds+FoLXdgguGaHg6iNpf0dI5GJVg7E0CIHZpFDQtbGigNks+M9/3iQcjuzTufcFIXRSqTSBuCCS0hEouKwadotKKmNUHQBDQ5oUwaB8+54P2EUglqIzmkagYLeoWDUTFpOK3aKSVaT1EaM2rM8CG/wwsdDwILcEE4Agz6Hhtfdd8eBQkk6n8Xo9+7XvkfVLsh+k02laWlpIpw/VrBOSgYK0BUkWaQs7URVDlOoojCpzM7zIybBCB7UxDR0VHZUGf4KPtvhZvKWT7W37N0XlQGR3drClJcrG5iid0Z3Ls+JkJwpOTWGYB/JtCnVhgcmqUj7IzZSpFThdZlpbQhQUORg0NI9AIM6zT67iw/e24/cbCUH1dUH8nXHsNo3RxxRhMimsWNaA0AUnnjQMk6ZSXxvkxJOHcfEl4wiFE5RH/Dh1fa8P9+0h0FSwmvonJltjhog8uVxhqEdh1mBDoD6/VWd5qyDPasSW7g5dCBRFwaQaMy1lPy+qhSqfgtuSy0XPEUgIGiLkhHZzII6CwG3rnQw1bty4A4x3FKQzOqF4ms5oqlcdVV3XiUQipDLGCILTaqLAZcFhMYTh4AJ7zturC0ike4807EoonsbfZUNeu5nmlBWv3YzdYqJ7X2iqEce7OQCjfAAKNrMpN2IRSWQYQPMpAcaLnc/nZejQofu1/1HvOZVIJBLJ7kl0PaSLPVZKPIZKqPIpLHF4GD9EsL0lTChmlAWKpxW2t8cp9VqP2KF+MGYtyg7nu20aPoeZij4qF+hC8Fa9oDVmxAxWeRUyQqCYFcoGe6lQvIaQCaf55KMdudqn8bghdG02M8FAjJLSIjasayWd1hl9TBEOh5lPV9ZTXOymekwRQui8/NI6xk6s4I03t+USpQqK7buteRpOQoVrz9epC8GiHYJAEkrscHqFgrdrKN9qUjilAhbWCKp8sKZDsM4PDpPgzMFKj1JWKuAP63S0BNGTKYqLXQT8cdo6Ywx22ake4kbF1KOtKV2wqFZw5iAFVVFIpnXawklA9Dmr1KBBlZx00kls2rSZVCrJvgRjhhNpmoMJY3jdcH3izwgcqok8lxmnRSOWTBJKZFCx4TBpOK1mfL6e99xkTdEcTKAgCOngsFlz1Rt2JZ7K0JKIYXMZwbSaw47DrODz9Vb4tRkdswVOzFd6iHKrM0NdRwwFnaSmUeIeGNUzFEXB6XQyffpUNG3/ZKYUpxKJRCLZLYmUjoKRpJLFalIocQie36ag4GJ2eYpoMvughI5IinLfkStOwRjMH1zgYGihY7fbqIrCGZUK6zp0GqOwNSjwWYzkIIsKGWE8yMPhOMedOJRn/v0pVruG12MjEIgxoqoQt8fG1s1tDBuRTyyWorMjSiCgUlTk5uOPdhCNJJg1dwyxWJpEJIHTZSESTrJwwXouuXz8bqccLXbAsho4vkSgqT2FXCwtMKvwYRMkMlDuhJPLe4omHcEmv2B6icKx+QonlRlidnsIXtkmuHQkgIIGNDdFeemFtdjsZiZPreSf/1hGKpXB6bFhNqus3kVM60LwZp2g2K5Q7lQIx9N8WhtA6DqKAi5r39dUWVlBZWXFPtxFg6XbA+QnMuioaCYVVYFUWkfpkssKoKRSeD0dDCsoQNMsHFvpId/ZO6A2EEuxuTlCLGG8sM0Y5u3yhO6kNZRka2sUT0pHR8Xpy6M+pnBZpYK9S9QHEkY5ro0BwdgxcMYgBbUPwb2+MURbME6px8zosr28bRxGSHEqkUgkkj6JpzJG4gVqr+kS5w5RSeqCN+tAsVgZ7lUIxtKEYwm2tESxW0zk7Wbmn8MdpWsOyV1F3e4Yk6+gKoJEBjYFoDkq8JgFFS4Fl1WhtiZIaaGDOeeMJhxOEAom8OXZKSp2EQrGcTgthIIJ0mmdbVs7qdvhJxRKUFTs5OTTRhCLJolFk/j9cTCbCaYSKKk09Y0hho3IQwhBWjdql2aH8UsdCl6LoDYMw3YJC2yMGuWlyp1QG4azintfZ1qHLQE4uXznMlWBbUFBmcsYojcB/s4kjz+6nFgsxbkXHMMr89eRSKQxaSrBQJz8fDuBWJrn5q/jvEsnsCqoEM3ACI/CmK6SVe2RpCFMEUwc5O4l9nYlmszQFEigC0GJx4rTakJVFOMFqjNOKq1jUhXiaZ1ANN1VBkzBqqnMGJFv9IE/TkckSUc4aaxXNSw2Oy67heHFfQtTMIbnx1V6WF7jJ51Os7I2yPAiB/lOM5qq0B5JsbYhjJH9b2JchZvFbQojvYYw1YXg5e0Cj0VhkAsuGaHsMeHJadVo22NvHJ4c9eJUURQsFkuftcQkRxfSFiRZpC0YGIlQRmbwhMG9s9YtqvEwdXRphWFFDlbtSJERgk9rQxS6zDitJsoO02H+Xe2gI5Jie1t0Zx3KfopTUKjOU3r83R43yiylM4LCAjvLP6ll9JhiVq9sJK/AzrrPmnnt1QTFJS4u/vI4otEUiqIwoqqAklIXeXkOFAXWrmpg3MQKQoE4Xo+Vhlo/LhMIVaGxNcZmi4+JBYJ3GgTjCxRG+owW1EeM4fq8PhKnhnsUhnsUYmmBxwKOPmabsqgKl1bt7CdVMV5iNFXnpHIjkzwey7BxQyuxWAqPx0o0kiKRMGIsM2kdTVNJpjIomkYwksHfEmL6UB87QkZJqrQOrXGBatIQXbPKh+MZvHZzLsYyntbRVCWXvQ+wsTmSi+Ws60xg0RRsmkownvX+Z2eoN+Kp6fprbOVOlV7ms1Hms9EZMWaq8tlVomETXq+vx1B1Y1TQFAGH2RCURTaIZRTynBZaAjqJtGBdY6SXz9NuNTOkyMn6oIlYGgq7RuRf3GbcpxHe/tlWNtO/OZjE60hQ4rEMmOz9A+GoF6cWi4XKyspD3QzJAEDagiTL4WYLoivZ5GBjNu2s2tgWSlDq7R3TZlKgLgJjLIbXaPJQH8u2+1GBtnCKtrAx5H9sufugt+/zprsdNAcTrGuMAAooKsUeG5rFwspWnQlFRsZ7NC36FHJ9UWBTKOjqzpTm5r3XYyxevIPjThiMxaJRUeFDUcDjtfHmfzYzanQxi9/fTnmFh1gizbIldaRSGa64ejKvvPQZZrMJq02jti6Aqijk5dsZXGzHWwIfNRklrUZ0e794bYfg4hEKHsvu22vXFIbs4bZ5zAqJWIbOzjAmRSUQiOMOJtkUtVM1zEdrS4TODiNBzuG0EAjEeuwvhICMwGYzkn7a2mM0On2YFCNG9+XtAq8VShxmynw2Gv1xNrVE2dyyM+ku69A3KWC3mIzkILq8/cbdIpGGZDqDQMVqNmG3mIxyVJqKt6vgap7T0me1ibxuHlK7tbDHunBK8Gmb4Nh8hXhaYUOn4JOkUcO1MWxHExrD7EkyqRT6LkMPg/LtuG0a2xsEFw5X0LomQxCCfgtTAJ/DTG27sf2GpggdkeRh+V3blaNenEokEsnhSCyZoTmYoD2SIpLIoCgwtMDe7xI2e0IIY+ajra1GgXmgh2eqO2cNVnhmM4wtUNCFMcw4eaiPNXXBrrg9QSozsDKJ94ftXcX2dRRGl7op9lhJ64ItAcEj6wRfqYZXaoySSPuK3aFx5uxq5j29mpdfWs/QoT5OPmU4kUiSeDzN4CF5KIrg/IvHUrfDT1tblCHH+xg5qojP1jbi98c5a041H31YA121L6PRFIVFLqxmmDmo73vXV+mm/qIC7S0xPvqwhpGjCln4ygbi8TQoCh6vjcIrxtPSFMbrNewxGknmPmcRAkyaSlqHYBKKCuyY3NAahUhKUOZUOL4UjPqiTtK6oDWY6DEza/azPwHOTAazakyIYDWbmDrMRzSZob4zTjyZodhjpczXd9JQc0yQpxhTpO488p7v5ao2qHQpDHIZ2430dd9XpTmmsrrNgssOY7wZYskMJlXBYzdj1VTqwkbrX9hqxPiO8CrMHrJv9uNzmBlU4CAYSxGIpnIe48Odo16cJhIJGhoaKC8vx2o9cmc6kewdaQuSLAPZFjoiSba1xXJD7rmhSQH1nQkq8mwHPKzXGkp2m99cobrMTYHLQlI3hjDBSJKxqAqJmM44PcQnS2IUF9gpKXXjsWtMGuLj4y0dKIjd1j/NDkma+j08/sXS3Q4G5dvY2GwUzc93Gd40TVU4vkwhg2BlK/jMsKZdZ2zBvlVpzAjQzApzzhlNMJQg0BmjvSNKaambjvYI+flufD4bdXVBhC4YfUwR+fl2mptCFJd6mDWnmsUf1NDSHAbAZtM4a/Yo2toiDPLkofcxhZJVg+1BGL6fc00kYhkWLljP9OMG8+qC9cTjaXQBKgJFCKKRJC63hWQyg9WqEQwmcDjNWK1abmhfVRXMZpWOpKDEbWJopRuzHYa7e5eVUhSF6lIXlXl2Uhkdu8WUs/O17RlC4Qwr2lIUmdMcU6AS01x0xKHQrlFduvdEIV2H12oFJlWQ1sGlGUlfXypTsJoU0ukU7e0dFBTkoysaazoEdWH4cllftmssK7ErlAxSWNEqCOsaFd6ebwP/qRWcXqkwxH1g9l+ZbyeetLCixk9GFyTTem4GrcOVo16cAgOuPpjk0CFtQZJlINpCMq2zui7cNVWmisVsJB0Ve6ysqg0QT+u0h1MU9Wdanj2QLbqvozCq1EWxx8LzW3X8CaOMVFNEMNSjUkWMhQvWE4ilsTssFHosZNL1nHTqcExujazECMfTbG+L4rGbcdtMxFM6zcEE9f4EQoDXrlFV7Nht2Z1DSdYOitwWNjVHjWoE4STFnp0vLSeWwSPrBCV2Y3h5X8noOla3g3kvbcJhM+FxmFm/oY1gIE5FuZvCYgfHHTcUPaNjd1qIRpMk4ml2bPdTNaqAmC4YN76MQCCO12vD7jCz+IMaxo4vZWhVXp/Te55eofBqjWD4XoaQa8OCCic9XngURaG5KYTZYiIUShCNpXMzASkKOBxmGmoDjD6mmBfnrWXW3GoWvbKBjxfvyH1OpjL4fDZ0RcFpU5l79hisdtNuS19lz+vqw0YmlahMKjFz9ggrO0LwRp2g0gUfNAnOHtK/xLUyp8I5zp7bbQsK3qwX5FthSgFk/bRv1QtKHHDBiN4iui8mFfXepiNuxPMeiDBN6oJVbUbc6zlDVGwWE4mkzqe1QcYP8hzWk2IMvF8CiUQikfSirjOei7XTUZg+PA+b2cTKNoHFAh67mXAsQTiRPmBx6raboNOY+UgzGTPVnDu050N+W1uaJ59dR57PwmkzBhMNJwj6Y/h8dvydMQZ5vVjMJpIpiCQFkfY4EN/lTMYQbCCWYUNThKlDB+6Usf5cVjfUdcbwOsy5h7+CMe/8tiCMzt83sZHUBa/XCipdJi46fzTzXlyPP5jEYVLQVPD7Y5w+s4qX5n9GNJKkuNhFW2uERDLNRZeOY+GCDbQ0h/F4rDicFjZtTBEMxBEC8vIdRFPQHhM0RmCI25gQAKCkqwLWRr9glG/3bd4agDfrBE6zIdLsmoJJhURTjIym0dEZMwR515SaGR0i0SRmi4lIOMHpM6t48/XNuRquoWCcq66dRDSSIhiMY/HYGVbpxrYXYZolrQuMbu/e5uwQvMJgN3x5JLzbIDAp8MwWwcRCGJO37yJwmEchz2qIUUOcGi8rDRGYNfjAhN87DTtnvtofOhOChTsEEwsVzhlqxDyPKXezujZIJJlhc3OEYysO39hTKU4lEolkgJNI62xuiSIwvCOlXlsu+z2Zgc1+o/ZjOKYQOAgxZ8VuKxuUCGkBoViKQpclJ0yXtwo6E1AWCeN2W5g4rpSFL68nldzpPbNaNS69fDwzhuXRFIyTSOv4oymCXbMoZUWe2WImz6bSGkz0qKM6EPHYNZwWlUhSJxpPsaYuyLBSD5sDCtNLFIZ7FT7rEP2eCjSLRVVy89GrOLjxmgk0Nobo7IjhclrweG20toaZc/ZogoE47e1RBg/xYbebcbksBAOG4A8GEwSDiVwFAbvDmOp0eatOMCEY4lZwdXtnUVC4bCSs79xz+06pUDhJGGWiOuKQ0o2QgHCpneatrRTk+XI6MVtiKxhI4HBY+PjDHUw7fhBzzhlNa0sE1aRQUurBYtFwOswUFjsJBOJEUrA1AaN9e++v1e2wPWh4Lkf5oNCuMG+L4MxBdM0yBU5NYc5ghWBS8F6DYEWLYJSPfZ5aFcBBnGO6TbDQGIWiAwzrDqcE/gQM3b+ZPQGjxNdQt9JDdLusGpX5dra3RgjG06Qy+m5jxQc6h2erJRKJ5Ciie4jBuEpPt6QngcMMn7YL2hKGFzKcODgiz2xSUdBpDyd7LJ9QAGcOVokGYxw3uYzXX91AIrFTmAIkEmlemb+OdCJDqddGVLUzrMSDJ99Hg+Kj05JPi5aP0+OhMs/edY2QyvTHd3ZosGoq04b5GFvhAgTRRJpEPEFDRPBho8CkKMQOsOt1oDmjssnio6DUzZuL61jw2mY2b2hj4asbiIQTlJa62LihlQUvrePD97fz5SsmYOs21K0oYLOb+fLlE0il0hxXonDmIJVRPgXLLsPbDk1h8l68d01RQX0EmmNGAlWJQ8FngZJSN6lkBpfbitXa8/yKorBubRPjJ1ew7JN6mpvCKAicTgvxeBI9IwiEk7z86kaef2kjzz21ipJktF+CZFKRwoUjFCpdsKRF8M8NOnYNPutDZHssCqdWKgzzGpn/0fTuQ3W6T62axZKOYFrxJJlEhEDC2Detg+MA3XqhFJQ6YF9msQJoj4tcElVnnF61hwG8DjPRjEIiLVhbHx6Q4Un94agXp2azmcrKSszmI7NYtKT/SFuQZBlotqB3e77smuxUYAW7yZjJCXYmGR0IsWSma9pSpVeJKpOqIARUljqJhBIkEsZw967PwHRGp6kpjD9hlMh5pUawLaRycZWJs4conD9MYXyBgtNq6ir9A+3hXeenP7T0ZQfdQxusZhMXDFfoSAiWtQosKrmSQcl+3IdQSrCuw5hpaXtQ8FmHYHmrIN8Gnc0hzKkkIp5ky+Z2GuqDmDQT7769lVNOG4HZrLFtaydLPtrB1ddN4cJLxnLaGSM478Jjufq6Kaze0ErKbKU9fmCCP5qG1hhs8sO8rYKOuHFdVrsxq9PGDa3MnludE6gmVcHttRGJpCgsdDB7bjU2p4VkKoPLaaaw0EkkHOPDD2oItIRRkylEPMmrL68nEcuQ7uq3yB6EpDF8rzB3iMo11SqaAgW2vrd3agrHl6qMzlN4pUbQFO25nQqkYhlqt3ay8pMG6rb5ScUzhjjq2Ebo/UcYLbbzdpOJgoJCNJNGqt9d2nebYinYn8gbr8UYuXh+i05aCI4r7b2NxWxiW8qNoprwx9J8sj2APzqwvlf94agf1ldVFYvlwOKzJEcG0hYkWQaaLaS7lWLqWfhdocAuiKcFFi1JBoFzN1M77gs7OuIIjNjWEcXOXuuFEJSUutm43pibxqR0CWhhfDaysE00t0ZZoXg5qdwQCZY+soUG8kQHfdlBzjOtKLlZguYOgcc2GLVEYxloixnxp6fuZSZNpwZOM8Qzxn7RtOGZG5MHbSE7oJBOpXOC95MltRx3/GCWL6vnmhumsKOmk1RSZ9vWDgqLnKTSGdava2HZ0jpmza0mbjbRGDJiRfeX4R7j/jRGBJ1x+LBJYFIEaQFei42xJ1WRCcf48pUTCQbiBEJJ8gvsVJa7cwlOrgofxYNcxJtaeO3ZVYQzLoLhDLpuvNQIBMFgnLraANHCArYEMmgqzCgx6sHujSEeaIgoVO0hZLnaZ8y49HGTETM6uUjJlcNa+Mp6EvE0qZROOq3jcGice8Ex+LZ+DEDi/QepOvHnmDQn7Yk99+faDsGSZiPsQBfwpTLwWXtewxCP4eld1yn2KRZWU42ZxtxWOK2yb9+iRYVCl8aQfAfbWiJEk4KNzRGmDfUO6O/arhz14jSdTtPZ2UleXl6PWR8kRx/SFiRZBpothLtK7wjoclHufMg0R2CwI006ZuTwF3sOTFSnMjpNgQQCFa/DjG83U5BqFpXBQ/NY9kkdOkbyCcIQtD6vjYSuoLnszBoEFrV/g3QDrfzNrnbQFEh0JaUpFHdzfamKgs9q9MEbtYKUDoNdsLdhW1UxEngAPmwUxDKCUyoUbCYw57lIqSZSqRQIUISguTHEqpWNjJ1QSiSUoKzcCFo0ayptbVFsNjOTp1RQUrZTGJY7D44gKXMqnDvMuMkpHcyq8TmQMBGyukkpUFzsotpiiDIhBDqGZ9JHhs6WANu2RYkIH4FgHIFCJqPnPO56RrB9aycnVnopdajUhvoetu6LKp/CB42Cap8RdrA7HJrCqZUwb4ugMSI4q0Tw6svriEZTBPzx3GQW0WiSJx9bwbVfuRBtw/skalZSffw23to8iB0pJ+cM3f1vwifNgi+PVMjoRhhEX2XdTIrCGZXw5KZ9E6cpXdAag2lDd79PRxzCSajMs+O1m1lR4yea1GmPGLHjhwsD65fgEJDJZAiFQmQyAzsYX/L5I21BkmWg2UKBy5Iry9Qe6TlEt8EvSCeTKAh8do3BB1iEf019ODcYmY0H7YuMEFRUeigpdeHxWHE6jOQdX76dqFCwWFXGDHVj6cdTJlvndNdZdA413e1AF4INTcYMUToKw7t5lDPCmG6zJQbnDVO5eITKtJL+PV4bI4JFO3Q6EoIzKtWcp1A3a1xwzigcdo10WieTEei6oKkphNdjY/myOhYu2MDi97fz4Yc1bN/WwegxxQwZkYe5H5nvaV3QGtvZ39G0EVKwd5QuYWp89lqN+M8KJ3g0QUYXuTjHrGfyuSdX8Pb8JdhtGolEBl3QQ5hmcbutbNnUhk8RTCxSKLTvTbhl54JSuHC4wnuN2QCRPbVe4eIRKhMLTezYESAcTtLZESOd0cnogkxGBwGxSJzNWwNox99q9Nf7D1KsxSCj77FdTrMhqj0WZY/1hs1q/yJOk7qgJSZY3S54bIOg1GHUT90VXQjeqNN5r0FwTpd4dds0SjxWQKE1lOy1z0Dm0LsEJBKJRLJHNjVHco9cm7mn6MkIjAriCOyWAxvSjyYzBGNpwJjFpmCXKYTeqNPxmI0M6SFuI+5wzjljePXldcSiKdJpHV0X5LstzD1nTL/LA2V0Y4bzeP+D+b5wDG+yMSnBMRWeXBZ0MCl4s07g0NivhKhSJ9g0Be8uQ8WFaobWZJrzLhrLti3t+P07a5gu+WgHo6qLSCYzpDM6xcUuSruG0TP9FPgpHV6vEwxxgccKmzqN5Lq6sJGs05/aoHsjW6g/FY2Rau/A57Ng6rLfXZtps2k4nGYaG0IUFjkprnDvNZlnS8B4ITi+1Bg6L3HAuk4jLGJvlFt13q8L5Gw2qxRFl3A2qSb8nTE6XC5UWwHJLSsYdlwTW235LGsRTCmGvuRlpUvBnwCbSWBS2W31hvV+2PN7pGDRDkEoBYNcCk4znFah9Mrw14Vgg9+oyRpLw8RCBYvJqGgwyAUFbistwQSRg5Qo+UUhxalEIpEMYFIZo7C+QCXfZcFr7ykYfVaF5oiCTVXojKQOqHxMbfdY0xIn6zthg1/HrBrDpSM9gAJrOgRpXaHaB2azyvEnDiUYiOMPxHF7bOT5bJjN/WtDMLbTE7yr8D7UJNM66S5P4I72OKDgtGk9hkc9FoULhiss3KGT2o/nv4JRS7PHMkWhvS1Ca3OET1du4ksnD2PjhlY2rm8hGEwAUFPj5/IrJ7JhawcxVN5pVxFCx2pSqHAac9UHk9AeN8pApXTB6DyFiq5hfrumcMkIY5aoSMqYKSojoD5iJN0IBLowpgUtthtxsaoCEwqUvRbuz15Dc1OIVDJJJtQKwLql6zn3/Ck88+RqYt3vu03LTb86fkIZLc0hygZ5esRa70paFxTYjHqmx5ca7ZlRAv/ZYQzv78lrqSgKTU1B8vPt6NlzdDuVEAIhFLw+O4H2NoZ480jGWhGLH+KCi+7hg04L/6k1Ym/bYuAwG/09xKVgNcGOkODjZpheYsS59kUoIRi/l5q4DRG4boyyx/JkL24VlLmUXM3UmpAhVD0WhSIH2Lu+U5GkMX3qgb7AflFIcSqRSCQDmHhKzz03hxf1Tk6q9sGKehN5ToV4WufjrQEq86xU5tuNclCK0vWwNY6iKEqvZWA8kJsCCbIZ+i6rxmgrjM5TyOiCmhCs7jA8bl6LUfYoEcvwwvNrCcUzOJ1WXA4zjc1hEpEkFouJSy4fj9m++4ehP5piVW0AFYHZpJC3m/jWQ0F7OMnKmiDt7QkaUkFQNQQqgwr6dnedUq7w8nZBUhe9SjbtKwIIRlJ0+GO0tER49+2tTD9uMNFIKjcLlMttoa7OT2dTiDyXmVlTvQQSOq0xiKRBpI2Yx0I7eCyQziis7gCHJsjrStAxqwojfdmz9ky069kaBV0IYhl4adveZ5UCQ8h2dsQgkwKho5qtNNR2MEVNM/fc0fg7YwQCiZw3+KMPawgG4tgdZjRN3WvMoT9hTA5gUozMfqdmzALQFt+zMM22raE5gsdtxe7QiER2HfJWsNg0XE4NTRVkon4A4ttXkOffwfGl4wB4u87wTk4pUQgkjBqon7YJ3BY4rlShcjfxvmldsN4Px5ftvo3Jru/ZnuvmCvxJuLBk5zYlXSWqasOCZAZ8FhMmk4qe0WnwJxhR7Nhj3wwUjnpxajKZ8Pl8mEyHx9uE5PND2oIky0CyhexwnGZSMfeRMBRJQ3WxgxEuE/WdMeLJDG2RNBbSBNqjhANxigodlJa5UU0KjQ0hOttj5OXbKSl1YbcrCAR1nTu9pqO7zUWe1AUdhtOQkT6FcEqwyW9klqupEMGYjk2FuD9KuE1H01TMFhPJZIbmphCDhuf1OTyrC8G6hhAqOg6LyrhKdy729FARS2ao7YzTGUkRS+mgmnG6fehCpT4MUcVCqN3MXFfPpDQwPJHVeUbJpWPz9+/8DRGdhjB0JhWGW834fHYUoK01wksvfJabBWrj+iQCOP7EIbQ0h5k0pQJdgMus4NqdvteMzPH+kBGiW8H6rgkCFKPigtcKaaFgVo0XmtaYTmMExhfuYpsCfD4biSSkrWWYTEYd0aWfNDJlxlDefWsrJk1l4/okwWAi5z1dvrSOueeOIRxKEtU02mICm0YvoVdoV7h4BDy+aadtmU09y67tDl1ASYGdT5bUcfa5x/DUEyuJx7OTVwhsNo3Zc0ezccU6Tp0oSIbaAAVVVQm++xDeC+8hqTmpcBlhBRZVocjeVZxfGFUNKnu/RwKCh9cJnBp8qWzPU5++vE2gYGT/j/TR5wtPLN2z5mpnwggFcJqN5VOKYEurEU+roNIaTkpxerigaRr5+fv5SyI5opC2IMkykGwhEDMemhZN7TMO0KkZT+Nyn40yr5XOaIpoZ4IXnl1DMpkBBGZVxW4xccKXhrJsaR3BQByz2YTNatSq9BWa2doaAwwR3H3e+NqQMdTrNBuPUotJQUdg0RQ6W2JYRIb21liPZCZVUcjLN6YxHaLk0dfo7I5246GpAuMr3bkZrw4VbeEk6xrD7JwHQEFRFaoHFdISTFDTLvjqNCc+2+59emYVDiTvZIsfkgLOGASZmJO6ZApNU0mnjZji7CxQigJFxU5sVo14Mo3XZ9vbofvF8lbBjpAgqYOqCOwmsGvGi4jNpDDZm2FYPMSKT2KUFdkpKXUTz5hIi56xwirQ1hwlndKJJyEaNTpFVUEXChOnZbjgknFs39pOZ2cMr9eO3WFm+dI6pk6rJJPRqanx463II5RUe6Q4OYkRwfBeL22FwU7F8JqS9TJmk6J6flcaIoJiuxFLqwiBL9+BvzPGko9quPr6KWzb0pGL63U4NNavbeLEk0ejv/U94wCKgmoykahZCR3boHgsJkVQF4ZAArxd3ujxhQoPrxMcr9MrGTDZ1U3HlSp83CwIp4xl2YSybJsbo8Y9OKlMIZyG+dsEp5RnZ6baeV2b/IYQbY4ZM06taTe205NGfPT25iShWCq3x8GogfxFcdSLU13XSSQSWK1W1H6WO5EcmUhbkGQ5lLYQS2aIdhXBbwkl8XdNRxrfTUCjz2rM5PRWnU6ZU6FMM/Hawk2k0sZQuS6M7OrmpjAL5q/jzNmjWPDSOlRVweu1Mf+lz5h9wbG5vOdd65qO8CqM2KV+5Jp2QTgpGJVvp7MjZiSUZFEMr2hnRwyP196nJ6s1lKC+I4aCTqHb3G9hqgtBLJlBYEzVeCAEY2magglC8TSaquCPptFRMZlUCl0WClxWPHYTIpOm1Gdju84ehSlAkc0Y1q3yQr4NssPh8QxYTXufPvPEcoVEBlQUzA4NX56dOeeMZuGCDQghyGRETpiedPJwlizZwazZ1TS1hOmw2ImmjPXBJHTEDeHiMhse1VKHER+bRWBUGOj+wjO5qPeMUeGUwKopBFujPP/0evyRDGbVEK0um8aZZ1fTEu8Z6pCIZXj5xbU47GZmzhrFoleMWcR03ZhmdcniWk48aRi+fAcOp4X29ohx/qmVWG0aLS1hQoEEeT4bk8t3JkZZ0hEiy57GMuXLJDUnSd2Ild2JIUD78ki2xeG1WsF5w8Ct6yz9qJYzZ1fzzL8/pb19LSedMgyny0JnRxSL1caE8YUkA81YEsGdPdZVair4zoN4LryH2rCDKl/PgvrBJPisfXs6P2oSnFAGg90KXosx61YoCu83COMFQBMIYXg9z6hUKOrKyi+yCd5r1ElmFKJpQbnTeGEwK8aVflifxpGJYc9k2Nyg59zHxpxxO7+AHtvACZvZG0e9OE2lUjQ2NlJRUYHVegCViiWHPdIWJFm+SFuo74zjj6WIJXViqQy7zuApULFZTHss63RqpUJjBFpjCg0tIVLJDIqiEE4r5FkgEU+j64J4PE00msLtsRIKJvAH4vgUOx0tERwOC4MLHP2qhThrMKxpV/CarZgtJhJddVi7u7gsVhMejzUX35olEEuxviGEgo7XrnFMWd8ZI6mMTiieJp7SSWUEuhA0B5LE00YHqQp4bBqDC2y5Yvj9JZxIs3xHsKvJalfDVYYVOSiyWqivC7B1cwcFhU7KKzzYXAomlb3GkxbaFY4vNearrw0b02UW2AxRuC0o8FpELivfpBjHtJmM5JWRXsPjbM8+lRVob4/i9li5+vopNDWGiESSFBQ4cDrNtLVFmTxlEEs+3sEJJw9HxxCj0TQM88DYfIVP24yQDLeZHt7rxojgzXojkWj4XuZ3d5kVUrEMry3YgJbRybcZw8nRNKRjaV5bsJ7pZ49HFybUrnjmpoYQ7W1RQlaN4Ic1nDVnVI942THHFrPkoxpKSj1k0jpWqxmrxYSuCz54bzuTp1RgtZpoa4tSVuEmjYKqgBKow//eIxQPm8ZyjiWVEQx277wf/gS4LX3fn/EFRv3YT9sgPxiiOZDA0RAw2hZNsaPGT3Gxk7IKL5+tbqShLsD04ysZevHPyARbEEKQTqfRNA1FVTCrOluDguvH9ByezwhI7iYxLpEhF+/rtRohEqN8xs3WMabB9ZiNihDZCJ7OSJKNTWEKdUF1mZsCl5nOuPFyYNcU2sNJ1jeEEOhgEqAbE2EYiU8Kw4scOCwq0aTxfTtcOHxaKpFIJEcYq+tCXXVLDR8HGEOYVrMJs8kQdQUeG0P3WrtUocwJlW6FlTUxbF0zMTlMgnRa7xoWNggG4rhdVsKhJHSVzYlHkkwaU9jvmb7zrCqqSaG+zs/sudUs7PKMZQ9gs2nMmlNNQ32AojKnUemqix3thsfUaVEZX+nuc9aaxkCCjc2RXuWGBGpXVUvDi+qPZYg2RjhuhHmvSTDdWV0XAkDv8hQ7LBous0pna4wH//4x8XgqJ6ptNjOXXTmRk9wmSJhR91Iea4jbKLO1q/fu1AojiUzXjZJTum4ImURGoS0Oj643vGrVvmzNV6MfF72ygVNOH8GmDa2kUhnWrW1G1wXRSDL3LlCQZ8PrUXL91RYTPL3ZyM4/vbJngtCbdTrxDFw4XMGh9T/rPtmluFQUnJrhefUnMMpZ+cPEC/JxagJNVWhpCaPrgkxap6U53CteVlEEx44t4+l/f4rJpOBwWohGkiSTGc6aU83K5XVMmlJJMpGhrSVGIpmipTGIR0lSOOsnhFa8jGncUKq8Tt6oEyQygmPzFTrjRpH6urAx5H1aZU/hOMqnUJWn8NHiGJlECrNm4tUF63G6rbicFjasayEYSuB0WMjoOigarcpwfMPHkEqnaO/ooCA/H5NmZkNAUO7sHT5QE6TPhLG0bgzjp3djPCoKFlXQmYBypxEi0hxMsLHReJED+Kw+yPFV+eRZFSLJDJs7EjT54yAy2M0qwwodaCYVn0Pr9X041GEz+4oUpxKJRHIIyOiCjohRIspjN1PotiCAIrcVq6aywS9Y2y5w70OSkC4gr0vIGvUbdZKJTI9EI4/HxuZNbWSfXSIjKCp0ou7i4czSHje8f7s+hF0aWO0WPnx3G7N28Yxla3F+6eRhvTzBya6nc77Lgkk1KgF0b184kWZzcwRdqFjNJpxWE5pJNWqhaiaCupUKa5JoMkOjP04yo7OuIcyxFe5+9pEgmRYIVArdVobk2WluihJWFf792EriuRJHApPJmC3o34+v4MZbp/PGok0cd+IQikocZLpmQepxbbrghS2CNDAuX2FcQc8MeBUj7tK9S3RAmRNG5xmZ8C0xKLXDcK+gpMxIEvv4gxomTK7IDY8DueH9L508nNaWMHlF9px39KNmwVmDd5aNiqSNOqxrOyCchvOG9j9UJZt1n8gIMsL422Yy4jttGqQyglBbmAY9g81qYtBgHy6XhYwusHUraZaNlwVjWtgtW9q47qZpbFzfQmfnTrtZvrSOyVMr2bi+hWPGlvL4P5cza041yz/aSqp9BxarxllzZlNtamJpaAQzShQUBda0G5UkimxG7KxTM/p8V0zA4BI7mz5N4XZbsVo1wsEEgc5YLlTVZFIwmUw4nRYWzP+MSy4fj2LuKjHVdZztIdErBCKtC1a0Ca4Y1fu8HQkjbnukM56Lmd2V2jCcXmkM+de0RanvjKOgk+fQ6IymUREs3txB9sqUrmCcIpeZUaXO/S4hNxCR4lQikUgOAfFUJpe6MbbS00Og1YWNIb4vVym0x/t/zOyc95qmEo+lCIeT2O1mVJPxALdajULn/s4YateDzOHUKC519RJarTHB9qCRAdyRAJdZYDVBoU0hrUNbXOekCi/hUJJXXl6Pu2uWqM2b2wgFE9jtZioGedH1nurUZdOIJVLUdcRpCyWJpXSK3GZGl7pIpnVW7AiSEUY90UmDe84H7k8IFm8VDB9qoyLPmChgY2OI1nCK5mCiazacPbO1NZoTGOU+G/7OJG+8tokJk8q7CVODTMYQqLFoivraABMmlfPS82s4+9xjiMVTFJa4qUmaWNmq4zQbw7aKAj4N9sGRCxhlnS4YAU0RoyTRU5sFw70mTps1ioUvr+OjbsPjoVCcQYN9KIrC8qV1nHDS0B6xva0xWNFqvNzEM4aNpXXItyrMGtS/hulC8Ha9oNqnoLrsxLriZlO68c9tBk3X8QfiWDSVlcvqjf10KCp2YdZMObvrblo2m4bTZWHVpw0MGZrHiJGFNDeGaG0z4k4nTq5g+dI6Tj2jio8/rCEWSxEOxbERJikEsViKRa9u5NKLR3H60ChJzYiRnlECuyYL9UX2O2KxmFi7upFZc6pZuGA90Wg2XETB4TBz4snDWLumKVd1onxwz/CTYMIQ6d1Z7ze8s9mRi+74rDDcFiXy8TO5mNldCaUgzwqbW6I0dhojDGVeC6NKnARiadbUh0l3+z7lOTQq8myH1bSk/UWKUxgQ5WIkAwNpC5Isn7ctRLqGSRWMJCiXbefPsafrWfPcVsG04mwGcv9Ehc1pZuaskbz0wmek0zqqSSEaSVFU7OSEE4fy4Qc1hlgQYHWYmXPesVhtPYeqd4QFa9oFI7wK+TaFcZrAaVYIpyCQNJJ9fFaFpSGNS6+YwDP//pRQMEGoyzNmt5u59PIJOJwau4bf5TvNtAQMxR1LGR7M1lCK9nAnQhjxdhazxrhKT68hf70rWSSeMfqjxGOltj1KPCnY3BLFoql7rZVqJJgZPienorJmQxNO986knF3JCqv2tih2u5l4IkNdXYCNG9vojKQ599zRXFrlIPtuIRAEErAPDu8cKgrlTmNYt8wh2BIQWMwKl1w6nq1bO2hqDFFc7KSwuIBlS2rZvr2ToiJHr5eLq6uV3Lz2+zrTk0BQG4a17YJAEjrjgpNLXRS7NGNo32QkSaUygmAwjtliwuW2EokkKSxy0lgfoHpMEXPOruY/izbh8doIBuIIsbPY/saNrZx+5kgWvbIRTVM58eRhFBQ46WiPoCgwaUoFjQ1BorEUCPD7Y5jNhtfSpEIqkaK5LYkvrw4Kqvu8jpG+3V+3zW5i5qxRht2Gk5w1p9qw3y7Rb7GYWLmiwajTCvg7Y1QOdfdIjkyLnvGt0bSgPiwYuZsasBZVoTyxDf+7D1M8bBoUj+21jdcCybSgyR/PCdPqrrJuPoeZqUM9dEbTCCHIc5qxH2ZD9fvCUS9OrVYrQ4YMOdTNkAwApC1IsnwRtrC1JYZAxWHVcFqNh0xjxCiqvSNkCI1qn8KYvH0TFyaTQjSSZO45o2moN5JoKio9RrKKCuPGlxEIxCkodDByVCFrkhYyUUGpwzjPRr9ge1Awa5CS8+YmdWgIG7MNNUUFq9uMKS9DSUFLvpNrb5lOc32A9vYoBQUOKiq9OFy9hSkYYQuUG1OlpjMCh8XE9rYo6W7j/1UlfQ9R5tuMovFrOwSVXY6scYO8LNvuJ5XJsLouxPRh3t3G1wkhSKR1BAqV+Q7aWiJ0dkTpaI8ysqpwt32qKJBf4KClJYzTYcHfETPKFsWTvPXqei69YgJq12QDCgq+PThw9a7o4t0VVw+lDK95KgMnV4A94+D1RZs45tgSNm9qY/PmNoJdU6k6HWbOOX/ny0VTVPBOvcCuGRnhx3RVQ+tM6ORZ+x7yzeiCJzYJrq5W6EjAJ81G+8qdCrMGw783Coq8GrPPHs3CBetJJjO4LArxeBrNbGL23GpWLq8nGkkRNMeprPDi74hhc5g546yRJBJp0qkMbo8Nt9uKruucfMpw6uoCtLSEMakKixasBwXcbivRaIpgIMHpZ1YRCRsvOz6PlaZovEccZcCfJLJyPo5TvtanF3JPxGMZli6pZdbcaoLhJIFADIfdTGFxAcs/qSMYjPd4MfLl2VFUjaKi4tyymbt4oAVGRYAzKvs+p54IE37nISwKuWz/7u2OpQVmFVpCCYQw0vSqS3t6a21mE2XeI1eQdueoF6cSiURyKEjphm+rwGVBURQ+bBT4kwK3RWFsgSEM+ust7Y4QAlVVaagP8cnHO3A4LTQ3hxg7rpRoJEUimWbwEC/lFR48eVYK/TqbAsZ86gBD3MbQZPdzf9pmDGEGk0bJmxE+GO0Dh6bQHBPsiJuIFRRwwtgiMrpRimtPM3kWuQ31pguB+v/Z+68gOa48zRP9HZehRUZqCSSQSAgSIAmQoC6yyAIIgKK06qnpqu6Zndkdszv3vqztw9rsfdlZuw9j02u7DzNWszPT3dXd1aVYZFFrDWqCILTMTKSMzAwtPdz93IcTqZAJEKzunuki8jOjGZEe4eF+/ET45//z/75PQHvMJl9t4PmSoKVf1SbqljaVxFRqSCKmwDY0buqP8+HFLEjJZK7OYNuVjcZdr0kOhWA2XSKeUGbsrQ+HCQRMarXGqvcEgiZtbWFKxTqFQo1wpJ0TJ2aolBuUSw4T4wU2bWm5plz7qgsvXZI8thGE0NCEqggvVD6DOtzZJYiaTfN7dG6/c4B33xll955eSsW68uNMBhgaaiWWsHCk5IO0asU4tEEp/s/k4Pkxieur831sUK5JiHVNVez+y0lJf1SR0htTattIQTbV5JBqD/Lt7+9kZrpILlvDtpUa/N23R5mZKSKBeq3Bntv6ePutUe7+ykaEAF0TuM21fdf1icVtHv/1MW69rQ/bMqg7LpouKOTrlIoOUkps2yAStshlawQCBqFYkKobAKqLxx1PWFQ/fZfQjQfWrEJeCQsCr3S6xMWJElbQ5MADG3nxuTPMN1sLYrEAyZYgwaCJ5/p0dEZXtb0sqO4XEDaU80HFXVr5WA4zN0J55BMsG2qjR4g1vVIXcKmkVgXylQYC+Y8uyve/Na7vswfq9Tqjo6PU6/X/3oeyjv/OWJ8L61jAf4u5sNA6lgiZVF3JSFFycEDjnq4FIYuyH1qQYJzMSKru55Mfz1dCmUjUol73mJ4qcvF8ht/99gQfvDfG+bNzHP9sGitg4km1dL+nben9tr46uebWdkHDh4Ah+M5mwe42jbCpolE7Q4JtLQJf+pyac1f1mF4JmZrk52eVglnXBC1hi7aofQ3+pZIWe2UfY8jSSUUsQDCWqfHhSJ43z2Q4n66sMB4/l17qN3U9j0jUJhQysW2DN1+/wHd/uJNAYOXnB0Mm3/neTt59Z5RgyKTR8AmFTQqFOp7n4/uSS6NZXOfazjtsCHrCGtmiz/jFHEc+mOTShSyNqre4DL9ATEEp/FPtQR7cP4Qd0NFtgw1b29l+YyfRhIUHvD6hCOgjGwQRU6ALVXF/ZIPGNwY1vj6oXSUGU/DoRo3HNgo6Qsqy6YUxyW/OS96fgf7o0nGYQZ2+wSQ33dpNJGrzu98eZ2qqgOdJfE8SCJq89cZFbtnTy8cfjiOlqjprmsD1fBLJIG+8foG52TIfvH+J/YeGsW2DSqVBLB5AaALbVkv/n346STBosO/AFt564yIy1MqC16hpm7SHqzQKs2Rf+ymFUon6WkkPa8AQkJmv4tRdqDdoFKt8+N4Yd929AdPUSbWG+Mp9gwwNtRKN2uy8uVudv+8yO5vGdVc/vCzgjs6VxHThu2u5Zcpv/BRfLiVYFV7/KZa71EoylID7e8V/95S0fyxYr5wCnne1Z/x1XE9YnwvrWMA/5FyYaEaFAhi6Uj3rQvVSLhdTfDwr6Y2oqMiyq6phu668+qz2J8CXPm1tYQ4cGubZp5cU3sVinXDE4p6vDBIKGzSaS+nBq1gKVVzJr89LesOC+3rhStXcna2Cly5JCg2fm9uW6h5CiFXVQceXPDcmOTCgyNS1QghluTVe9rmza+X7NrSGmC85CATFuvItvZStUaq77OiJMlt0mMjVkWjEQyb9qRBEAjz+y8/Yf3CY5585jaYL/umf7CE9UyKTqdDWHiaVCvPuOyNs3JTiyMfj7D8wzDtvjYAEKdQ5xeIBZqaLdPUrR/jLz3c5DCEY8Mo88VcnqFYbGIbAMHQwDb7+6FZS7cFVVlWKGGrk4wmcCPS1Nj8D1VoxWoSfbLvytQFVpZ6sqCSiy4lqxZUcnYOWoPLoHE4KeiPw12ckHaHLlq+lMu8PhS10XaPh+krhrgl8X5KeKS1GkI5ezFB3PPr6E9RqLvlcldGLOUBFsr737hj7Dgzj1F3KpbpqBwlZjFzMMLSljQceHOKZp06Rni6RTAbRrQiWcDhwcAv1D/6MogOV85/QWRjBDey4ysyRlF2IGoL5WdXvXCjUlZWahHOlOg3H59FvbF+0l3IbPpatc/FCBsvS2X9oGKNpPpquKgeL1aEKS//O1CQfzUq+1icgc5Ha6BECzXhVTayuni5ck2oz1S3xOb3TX3ask9N1rGMd6/hviHShztl0hQVRTsDUEaj+wLM5FpdUG75EF4q0AsRtmFpbs7MIDaiUXebSZWzboLs31iRbRUolh9bWEKnWENXKkkfm1TBVlrw7rczaN19B6LEAS1OtCC9dkvz6vM83BjW8mlI6ZzNVki0q7rJh6jw/JkEoZfK1QEOlDs3MlJifr3BDJEijGsVe5jkatg1u39RCoebiej7lusdktkq24nJkrIDbFFEtuCNoQmBaOl99cIiXXjjDvgNbqFYaHD0yxdCWFIObWpieKpCeKbF7Tx+ZTJlb9/bz1hsXSc+U1IdKsAMGkYjN3GyZjs4oE+P5Fedb03VevOSzt1Ojx5bkSw5/+7NPqFYbCNEk3EKQSAR47ulTfPv7OzGDK/sKF4hOT5gV9kVjRcmrE5LHBldXuxcwX1MCp4sF1ZJxc+tSL+oCQsbCg8cSfnvBZ0fL2vvVNcHURJ79yzxuNU3Dc30CAYNb9vTywrOnqVQa2LZBLlth0+ZWPjwywf6Dw7zwrHpPZq7C4bdGFsV6r7x8jvnZMqGwSaXc4JOgwd47Bti6rQ3Pl/SkumjxLuJ/+GekJy+QsJW63n272cPJ8t7ThRkumKnCyXm4K+Xx9JMnuO32fmzbUMeNsl27eCHDrpu7eeqJk9i2Qa3WoF5X0bBOw+O5p0/xyDe2ciIjOZWXdIbh3u4rfycmyoroW26Zwus/Xfy7J5eI1+W9p1XHo1Rz0QQMpD7P2/jLjXVyuo51rGMd/w1RqquKrI9gZ198UU29JQHPjSqhT9IWXCopAZAmBBfykreb1lJXQ6Puc/b0LKnWMC88e4Zb9vSgawLLNugIKaX5U0+c4NCj2z83Z7vqqurmd4eWsss/D7oQ7OuHz+YFE1MV3nzhNA3HWzQbsCydvQ8O0xEKcVvHgs3Q1Y9DA+bTVZ596iTlskOjIdENwZGIxYGHt62oNFqGtsJWJxowODVVpFT3FimWqeuLwhpXgmlrHHx4G+l0CcPUGBpuJRyyePXlc0xNFtE0QXtHmNv29vPUEyeYmS4t7j8QMHjo4FZOHJ/mxl3d/O7xE2SzS32RlqXz0KGtfGNTkNnZGoWa4NyZWcplZ+kEhUTXNYr5Goau+iH7BpOL4zJRlrw8Lvn6RrEifvR0TjkqfHNQrJmKNFWWvDMtiVmwOS54bFBwbE7y3oykK7y6Z3I5PplVvqiX+3guh65rHH57dNHjtlSs09kdw3V93n1nlHrdpe54uJkKM9NF6jWXrz44RL7c4Af/5CbyuRqlskOqJUhHd4zpyQIXz2cA5QIAgkKhvmjg/+i3djDQbcGlEZx7fkKXp+ys1BiCpfksG1VOqhZktrdARxDec2GqGSZw/Ng0Dx0c5pmnT1OvKUeASNSiXHYwTZV4FgpbeJ6kUmmgGRpOQ2dsqoLfGufQRtH0Ub0ySg01hgtVUwBDUzZcTf0jtdEjRDMXoX0HIJjI1RBIQqb2B2ea//eNdXK6jnWsYx1/DyjWXCayNaIB5T24AM9X0Zu+hLmiw0ROLSuautZcupOcysL5vGRnq+DNScmjGwUzZYkhVBTmkTnJD7eIq0Zn6kKQnq8wNVEgEDC49bZenn36NLatr0jgOfTINiJR66qk8GRW8lFacmfXtRPTxeNAMBxw+etfHqdScbFMtWzteT6Visvbz53k0W/s4OTHWcKJIH3dCxVQZUDfHlz5ebMFj2eeOMHcbBm/uaSsC6iUHJ568gTf/6ObVlUaF9AesynWXCazVdQiuMBc1jYhpSQSsfnVz48Sidm0JIMMbmpB0zQGN6WIx4OkWkN0dccwDMFd924kM19dETZw7LMpymUHz/XJZCorVN6Ooypu3/zujTjFGrlKYwV5VQcBvucj0Gg0PHLZGgNiKW7UlxA1WEFMF6Iuf7xNrFqiHylI3p5WGfMPDay8fjVPsjV5dbGJJyVH5iSPbbzydfd85RVaKNQXPW5DTV/bZ586g+O4pFIhymUH6Us8AfPZKiVhMmLY3N+t0dYdRSAXezBTrZGmIG2BYi7ZpzUaHi3JEOPTVbo23celgsd0VfU+m5q6jqXLjjFswomMqjbHbYEhID+vxj6bqXLi+AyHHt5KPl8jm63S2xunUnYwDA1NE+RzVaRU/bI0K9xOoc4t23Wk/PzvxJaEYDq3smq6KvFMSiZf/Cmt3/7fwQozX6wDksG2L+Y+8GXEdU9OTdOkq6sL07y++zvWsT4X1rGEa50LuUqDc+kKvpRUHWUSNF1wmC7UCZgatYZPxfFWpCRJNExDY6gjgo/koxllcn97p+DtaUndg5+dVqbuAV2Zdz/UvzYxXejnREKp0GBqvEAsHuS1Vy5w/wOb2H9wmHLJWSRTkahFqi2EGTBUPOMVsC1J08LqixFTDcjN1xm5mCEzXwGp9NXSl8TjAQwNpieLnDs9x9kzs5QqLqGgwYFDW6mEg5zI+KpHr7mvRt2nOKOW1hfENaJ5WL6UzKXLTE8W6d+cvCLZHmgNoWuCesPDNDS6E4EV2+2gzv6Dwzz+68848dkMZ07PsfeOfkIhg8HNLcp+quby2jujDG5K8eknkximxpnTDkIILEvn4Ue38dwzp8lmqsTigcXeRACn4TE5XkDTBbOzJeLx1cu1Uqr/PFeSbAmuMNQ/npEc2LDyOnwwg0pGWnZ9fCk5Ng+fzitiGbusmiqRHM/A94dEs8d4be/cuZqaiy2BK197KSWRqMWBQ1t59ulTFAt18rkab75+kQOHVE9uw/VxG82e5qDOvv3DbGw1ODbiNav2K6+XaWv84J/cxN/87JNljgmSYNDk+390M/V6g7nZChsGkwR9l2CmwNHRKu2tqn3CvixWtjcMmZrgNxckXx+EcmMpPQ0UQX3zjYvohoYvwak3GN7WgdvwyDd9WRfG1dA1JCrJrFED4xraUVIBgVG/SG3kkxWJDAszw/MlJRdC459g50cotWzHcX0EXPdKfVgnp2iaRjB4ffd2rENhfS6sYwHXOhfmSg6luodyJdSat3tBoSYpNm+wclmdKhY0SYZNuhMBDF1ThCID+/oFqYDg0Q2CUkPy1Ijk9k5BV2htgrDYg9ns5xzYkOTIxxMqmcfSyedqvPryefbe0Y9t68QTAWxLxzA1LNtg9HxmzRv6En4/xXC96vHuOyPYtqEEQ1LiNUuAC2Is35fkcjUaDR+v4VGRkueePsXXv7eThr9ETOfTVY4emSQQNHEXAsmFWk5egC8ls7MlNm5pafaUroahCTa0XtlaakHUcuCg8oXN52tUyg1MU+fsmTm6umK89sop9t7Rj5SSAw9vXbSUMkydgYEk0zN5pqeK6LpGIV8j2RJEaAJUay2Z+Qq2rWNbJqGwudjvuBKSUNhcYahfcCQ1l1WJQyMFyXeaLR75uhJEnctL+iLwgyGxpvG+AG7tELw6IdkUh9NZyc4UbIitfG1HUNBxDT+DhqXR0Rnh0CPbKBRq5DI1IlELO2jwze/uZORihrm5CvF4gFDIpFJxqFavLDIMVcZoRNr5Z/9yL1OTBebnKotVa6fh8vorF7jxpi5Gzud49ulT6rumqUjYhfaJ5S0euqaxux2Kjs+HadVv29GjkqGcZgCGRFKpeZRyVW7f24cmQDe1lRVOCZ7nEwpbhCM205OFFW0XoObhZ/PQGoT2oLo+nUaFypv/ieULAeWmzVTdUy4VURNMHdIv/5T27/zbxceFUt0j/LmuFV9uXN9nD7iuS6FQIBaLYRjX/XBc11ifC+tYwLXOhYUKlwQGuxK8MC74+gafSs1lKl9D1wQDqRDxkAnNCszCO14Y88nVYWNsKQMdIGIKHuqHd6YlXQMriYMQinjMTpd5tmmIHg5b+J7kzKk5tu/o5LlnTy+qzxf69UJhC9/zefjrOzj81ggT4wUsS+f+h4aphEMMxq59bI5lJLYGm+KsMEUXQjA5VSSbrzG8pQ3P81cscfu+shvSNCX+OXMqDaAU67pgfKLIYFsCUCT3qSdOEI0G2LJ1mRF5cwlc17VF/hyNXqOq6gqoVT1OnUhjWcaiL+yZU85iDvw//5d7KeTrPPO7k4TDFuGIhaYJZZ/kSwY2JCkXXaRUwhrD0HAcr5nProhOPBGgVm0QCpt8/OHE4vVZTlCDIZOHH9u+Iq3r03m4qVWscjxI2vDUiGS+plTjg82e0tXq8eUQbE9KPpgBQ0ge2bi6JeBq+HhWsimmlshBNUnEkzampWNMa4RDFg3Xo1Ju8Nqrx6jVXEIhi7Nn5nBdjwMHtzI1WcC24rw37dMXFbw2IdkcF9ydqlB48d+j3fwjpirtmJZNb08I1/WZmshz9Og045fy3POVQf7iv3xEve4iNEFLSxDN1BfbJ779/Z3YQX3Fg1ssFKS/PcL9vQaWYEWYQMOHmg8bB5OYpsaFCxn27R/mN7/8bOnaCCV6239gmGNHJxkcamVAJFn+LORL6AjB2bwKMQgasKld4t/yTRL2N9HVcwqyJmkLCAoOJDUIGko0VXJVz+wCyvV115jr/g7seR65XI5wOLxOSK5zrM+FdSzg8+aC50sKNZepnCIw4yU4P6FxX6+gJaTTEjLpbbm8/LREBOqe6pvsCAmilurzW04sErYgYakKzIaYWKyUTk8WCQQNnn7yJJ6nTM3j8QC5XJV8oUapVOfW2/r44P1Lizns+XyNZDLAxsEUjtPg4oUMpqlTrrn8+slTfPf7O/kit4JNMThfgN+cl7QHJVELGr7AE+DPVJnPNwiFLWzbWKxQLZya5/kEwybBkPIJDYctJNBo+GSzNe7apuH5MD2ZY3a2TKnS4M57BlZUGmWToAkhsAMGHZ3RzxV3XQm6rjE2nsOyDEJhk3rdo1AornjNW29e4Hs/3MWv/vYohUKdYrGuPtvW+c4PdvHm6+cZGmpbPEbflzh1DztgUK04RGMWra1hZmZKfPDeJXbf2stHH4yvvD4tQYa3thFr+pYuwNYhjs+lC0sOAC3tUQQabUFlU3Q1G7DLoQnBH20Ba1X2uyRTg6kKzDZbYiMm3NS6FH9adeGNKUnNlWxJCHamBHOzVZ57+hSmpbN5S4r2jii/+OtPcZvL08VCHTtgcPDgMMePTTO4qYWWDQnGCopYP7JBieIWRENGuUBs97/mjVcuYQaClP0o5TqUSw4PHdrK9HRxaR74kmymSmtbGE0TOI5HerpINBbk6d+dwHE86p6qUqbCOgcOKfHc8jCBTKaKFg6yY3OcIx9Ncv7sHF1d0RXXJpFQvcWffDxBsVjlllt7uXy6GZqgM7QQZLEwtlHcwXv5qzHJD7dogOTp05KbYnC2oMa34EB3VHBzK5Q0AajfE8e9csvN9YL1O/A61rGOdaAIZ6bcoFR3qdcdylWPWM3FsixKdY9S3cM2BNN5h9mSs7j0l3M05nybf7X18ypXSzA1uLld4PswWYbfXZQMJ2Frcsnv8NYOeGVcMl6GHXqNp544gdvwuGl3DzPTJYSAWDzA/FyFvr4ESHjjtQvc/8Bmbr2tj2KhTt1x6e2LY9sGxWKNV185Tz5fByGIxwOENZ96poRIXLln83IEDUFvWPU3mjrELUUwWoOQ7wxyzoQjHy9YBp2hWm0s9ouGQhb33LeRd94aUelBusBpSNyGR1vc4sgHk3T1xJhJl1RPXrFOpVRf5dcqJdi2Usp/nrjrqtdB15iaLBCN2VesaI6O5vnq10z+6I9vYbbpf9raFibZEuL9d0c5e2aebKa6+N5GQ1VNC4UabW1h7ntgMx9+PMHQUCu37lUPDrfs6VlM6xrYmKC3N044auKhKtCuhI/Ski63yjO/PrVo8F9xJb6u89ijWwklg5QaKu40al7dq3Y5Liemc1XJp/OSckOJeIYT0JAwV4XnxiQPN/td93bA2ZygM6z8dkfmXV787Ukcx8MvueQ/m+OugMW+h5QlV6nkEI0HiMUsjh+bJpOpctPuHoQFF3yoeSqdKkFlUTTkzl3A/PDP2HfPT5jXNzE+VaOjK0G97nLqZJpweGX0ki8lDcfDDqo2kvlMlWOfTVMq1nFdidAFYVOn4fgrbLoWwgSivUlys1WmJ0sIIcjMKxHUC8+exrYNVUU/7VApN4jFbQJBjfZlaVHpqmraaQ2uPfapgCBmSYqOxGpq9oqu6iHXNa4ocEyG13UP6+R0HetYx3UNKSVT+Tpj8zVqzYpFw3XJzLtkZAnLrK/yBJXN/tJowGBzp02XE+DlS5L7ei+/4awtOtGEoKW5Gt0ahBtScDwDvz0vQSiVfMyE/f0al+Zdfvf4SebSJTZsbFFCIxRBK+RrxJo9fYahMZsuc/idUXbe1EUgoKPrGq7n0RoL8+Tjx5lNl5o9m5JSQVXsctnqqmXKq6HgSJ68KDk4IFbdlAOdqqcvn61y+J1RDhwaJp+vk8/XSLWG6O9P8MTjx5mbLYMQaJrA93ws26DR8HjrjRG6uqMMbWlToyYlr792ga98daW4qyUVJBYPEmwJ8X5WcEubvKqTwZXgS59UW4g3X7vI7XcOrK5oJgMMDbdx/vQczz17ht6+GF3dcUxT5y//y0dYlo7vSdIzZd59Z5R9B7bgOJ7qkZWwcVMLmUyFi2fnqJTq7N7dy0MHhpmdLWGaGl09SZItQaJxG4mkUXGZmSoyka4yvCnJM8+dWhQVuVL1TcY0j+eeOcXBb+3kw7xGUFcVuP6IeqC51n5hH8mns5CrS25pE6uspXxfcqEAj5/3SQUFPWHY1qL2f3un4NKFPJrnYelQcyFfcjAMwdPPnsOydTo6IkzNlEjnHDwpsW0dLxbhTEZVVW9oEbxySfJocMlqCWB+4gItHe9hbttJKASFQo0XnztLLGZz697+Vefhuj4BVCBCOGwxNponm6s3bcqUJ2uiKYJbbtMlpSTkejz+8jnu3t2FZenYts57h0fZf3Arzz9zerGKbpoa4bDFgw9txlympwsZ6gFyKKFEhGuN/WwVAoaytmr4cEenEle9PSW5q2tlIIIQav1/XRC1Tk7XsY51XMdwfcmnlwoUax4g8NFIhi3KNQfTDoJmIBHN/+BCUdAdgq0dQbqTwcUlz05grAiPn5d8rR9amjf6Z0Yld3Uu9eldCZpQeeY3pGC0AC+OSRI2dIchlC1Rrzj4PpTKDv0DycX3SQm+J/nw/XH2HRjmpefPkMtWefZ3pwiFLbq7o9x2ez9PPn580Th+oYrpS0mj4ZFoCa1aprwaIiZ8b0g0Y05Xwg4qYcrTT5xgarLIU0+epK09jGnqbN6colCokc/V0DRBOGJRrTawbIODD2/luWdOU8jXCIdNUq0hWpJBLFunWKjz6kvnuf2OfgIBHU0L0NERoa0jgh3UyWclvz0v+fqmK1eiFuBLSd5Z8vhsuJLe3gSFfJ133xll7x39ixXNnt44lqVh2RrvvXsJgPFLBYpFh9GRLAcODfP2WyMUi3V0XTCbLvHKS+f43g928e7hMcYv5SiX6gwOtaLrGrlMleeeOY30Ja1tYaSAI0cmufcrGykXlfL/6d+dwPdBBC1sIZmZLhGNBSg3rYsStkqYwvPxcyX29SdU6hCSNybg41m4pe2Kp8/JrCRXV6KcCwW1PH9z29quDK1BODAgCOjKUD5bg99ekGyMwS3tgkymiidRwh4LAprPiQ9Oc+hhZcw/M1XEMDVM1ycRNnj4sa20tRpsRIm43k9LbopVyL340xWfm9x2L9ruf8npj2cYH89x2+0bUN6n9TXFZIahHhRNQyMUsigUaiy0drsSkHJRpLb8QUwI5SnbEreZny9z4th0s/p9ZvFBo1JuUCzWGNiQpKc3jhRVfF+iaQvfBcHBAfggDS9ekotOEwtYiBs2NfW9AXh53Cdfhw2x1UldIUunWveoN3y4zrW51z051XWdaDSKrl/fhrfrWJ8L1xsans/HowWqDR+EymbPSZuCZrKxw2dHp4UrbApVj4ClI3SDsXHBfUNr2TpJqh602MpI39KUTNvWWVzOuxYIBBtiSkH98ayPKwWNUpWG6yvvxWx11Q3a83zm58u8e3iUhw5txbR0LpybJ9UaorUtTKXikG96q0KzX7NJdgxdo3OZOvxaoAmxaCJ+OXygrSPEoUe3Mz9fZuJSgXgiQH9/gtnZEoVCjT/+0z3Mz5eZHFfb+voSvPn6hUXybFk6tqVz7/2DjI3miG0LEAqbvHt4DCFg34FhunuiYAh8lOVVUFf58oNRSX9MYF6BpL42IRmIicVkKiklobCxaGG0XEB2xkursIKGTyhsksvVkFJSLjnYtk4gaHDv/ZvIZaqUyw7RqMXQljZeeeksp07OAhCNB/j0+AyHHt3Gyy+cZX6ugu9L8vkalq3z0MGtvPn6RXxfcsddG5ibrSCFYMtQiHyuSsOTZPM1WlPBFeIzQBGtTUk8X6IhuK9H8uyo5K0p2N229jJ/zFKWSrYOhwbEqn0ux/JY2f6I+m9XK7w3A78867MlrCyvFgmz6zA7MsZJO8hDB4eRskmuozaRqM17h8e4/c4BUu1B4rbAl5JNcYm395tw2zcXP6uUupVf/PwYO27sZvK9cdrawk3/U5f3Do+taL1YSPmyLJ0HvjZEfrmHbNMpgSs8iGlC2UmFTYjFbNIzRd5/d2yRlE5NFmhvj9DalsI0BC+9eJb77h8kEFo5+Q1NsKdd8uRF+HDGZ0/HUtXzXB5uSCnyb2k+SRvagoIHelePu+dL6s2VG+dalzG+xLjuyalhGLS1XeVRcx3XDdbnwvWFsUyNasPHR2N7V4QzJZOcA3d3C2wXZtOS7HyGZCpES4eNHdTRhL9mde5ERiX53JAS2JokbAksfXVl5FqRqUvO5OAbgzBXCaHrAt/30Q1j1Q1a15V5ezFfIxQ2CdgGU5MFJsbzbNvewWdHp1a8XghFaG3b4MAj27Ca7PnvS4LhSShX6pw9Pcctu3uo1Vw+PTJJZ1eUrds7mJkqMj9XYfOWVgK2zhPLqrrtHRF23dzDz/78Y0xLV96fno+hCw49uh3flwRDBrqp4S0j1BtigpgN0yXJC2OSu7uWq8ollYYir3kH9rSvPF7D0oglrDUtjGpVh9deucBj37yBudkyIxczxGIB4okAvi8p5KoEQwatbSGSydAKYhoIGESiNpkTacItYfY/sp1LFzPks1UiUZtozOatNy4yN1smHLYoFVVlsFxuMDNTom+DqpBrqJ5cq2nPtYBEMnhZxVtwYACOZdRS86ENq+deT1gtz1+OqisJGNcyXwV7O4AOSaMa5bOQEr0JPLyiOu+5mRIvTJzm4Ud3kM/XmJ4uUi4pY/3lfZ+TZXhyMsz+jV9Z3LumaYwfn2V2rvkQFjB48/ULfPcHu/jF33xKeqa0WNWs1VyEEERTIQZ6o1y8kAVNw7SawjS5FP0Lqx/EfKl8T48fn2bXrk4Ctkl6ptx8QLGarg2zOI7HD390MxPjec6fy7Djxg70ywwiBOqB4NUJyDo+LQHBQASydUgFVGuP6wv6o0vxxAtwfYmhQbXhKYcL1gVRsE5O8X0f13UxDANNW+/zuJ6xPheuDxRrLqPzVeZKDRZux9GgSXpW9YN5uSq/euoklbKD60oMQxAOW3zt0DZ6IoFVynpQkZBL5ua/HyFdgI9kqizpDqkKV3tnhHDYopCvo2lq+XjxBl1t4PsSw9AIBE0ajouh6xTyNQqFOrtv7SWfq61YpiyXHCJRZYnk1F3On82QSCoVs7zMsuhyXG5ptICZqhLUdIVgpiKx4lGmps7yy58fpb0zwt47+ikVa7x/+BJd3VG239jBe4fHSMSDSzn1wN47+nn+mdNqud/SicUDICWuJ3n+2dN853u7KJZqaogvO7wWW9BiC7ojklcmJHVP0hZU/Zi+hJvbBIOx1dfG9aFYcNB1DV0XdPXEcBse45fyfPj+JW7Z08vzz5wi2RJk7x0DzM2WSc+UaGkNsW1HB47jMj9X5YXnTzfjNxUx3X9wmHOnZ/nK/mE+ymskJ7KcODaN50lqtQYNx6dYrGOYGp7nk8vViEZtCgXVoxsJW4SCimh5roRlhMiydDqWCXOWXSFuaIHtSb4QjmcgW1+5LO1LqdT7FXV9TU2JqUKG6rVsDep89cAwzz59CmpVfKeK0A00XWffA0Mc+WSCbGZlGpbjeIt9n/f3KM/VsisXU6x0DWbnygB88N4lHjqoWgQAfvTj3czOlslmKtgBg6EtrZhhg1+c9Rmdg07d4LOPxxfFc07dhWYyWyBgcPDRbQSCBq6UzFUlOUfS0xHFrVWpFosceHgrzz2jBHyFQp1CoU4gYPLQwWHK5TrFQp1stko2U6Gte+XYG5pgQ0zw45gkW1d9pu9Mqx7hdBWGE5K3piT3dgv+60nJT7YtjLPk52clXx8URGyjOaU1JnI1Btuu7M17PUDI31fm+A+AP/uzP+MnP/nJf9PPrNfrTExM0NPTg23/3fzy1vGHjfW58OXGTKHO2HyVsqNiLIXQaI/btEVtEiGTsiuZz/u89usjzM2V8f0ln05NCFJtYb79w5sIR4wVN6bjGcl8De7tvnZSuqCwvpzISiQfpiWTJdXX1xdRNlJz6QpP/vo42WxV2TDla9i2wcOPbWd0JEMmU2HHDV28d3iU+x/cTMPxePrJk8TiAW6/c4DnnzmN47h098QIhSzyhRq33zHAic+m2bylldGRLPsPDZPNVKiUlR9nqjW8aNJ/uel/smUplee1SYkhJJdKqq+uPyrYkhTkp0o8+dvjzM9W0HVBR2cU09R44GtDPP/sGUqlOvfeN8gLz56hXncXBS+vvXIOz1OEO54IUMirlgTPkzz2zR20tIZo6/r8VoSyK2l4ELc/vyJYL7s8/qtj3HprL64nmZsr094eWcyJ933J3GwZw9DYsrWNVGuQoS1tzM2WSbYEyWZq1OsubsMjGgsQjSqBk65rtHeEMQydc2fneP7p0xQKdWxbidUqlYbqvw1b3HZ7Px9+cIn8gj/uhiQ37+7huWdUBO1C5XQtw/kwVcpfoElxoiwZKyqBTksABqLKMaIjqFT06ary4OwKCTYnVDtAwVFE3vGVkl9KCJmCTVadubNnyKRztLTGiLR18eZLnzHvRLCMZUvgzcu1+9Yedu3ppuD4/OKsZHsL7G0uhWuaxtkTs/zmV8fQBLS1hbl1bx+VcoNcvkZfb4yNm1KEYiaNZnVRPSxCo+rzq58fJRyx2Lajg3xO9TcnEgHCUZtYzCIYtGhpD/Lnp308H+5urZGYH+fw2xfZ/ZVbqDqCXLZGPl8lHg8SjpgEgwZvvHaRmZkyX9u/mUDQ5IabOpFN4nu1eVhwJL8+r8IRZiqqxUcXkHcUEY9bMBAV3NQqqDgeH13MIvBJBA1u6v8C5sN/QPjZz37Gv/pX/+pzX3fdV07XsY51fPmRrTQ4PV3GkxqapvpLe1uCRJalsERMjflcnvRsWfVlLnu/70tm0yVmJvIEQyatrSHsoEHO8Xl/RvKj4bXIz8JNa2WV9akRtcx8X6+g/TI+UXRUYtHBDSz2TfpAW3uI7/3RTUxOFMhmq7SkQiQSQaanCgxsaKF/IMlbb1wkn6sRjdi8+tJZ9h0YplJ2KJVq/PCf3kw+V2NqqkBXVwzH8Xj3nVFq1QYdXVGEgPGxPI7jkp4pkUgEqVZc2trDxJM2c+nqonH5AhZI0j3dQabLSsy1PDKzrT3Mt76zkwvn5snlagxsSNBo+Lzz1gi7b+3l5RfOLhOhnCYStSkW64uV4GjUxvcljaZaXQjlm7l5SytCSs7l1TJ9xISeCCsy5KH572u6w0lOVg3uf3CIX/3NETRdEI3aTIzn2XVTF7omKORrCAHxeIB8vsptt/fxxqsX2LCxhWOfTbPjhk50TeA2JeKO42HZGu+/M8quW3ro6IySSITQDRUe4HkSy9IW51YwZBCOWBQL9cXZksvVOHFihkOPbMMwNPL5Konk6qjOaqVE5ZNfYu/+DnogsuYZXg7Phy0JJRiaKKle3P4oRAxlJt8VXr0CEF3mbjQUX/p/K30WPvz/0hFKQEYj7f1zcukMRiJMperie2r1wbJ0NCGIJYLk65I3JiWDMUWQF+D7Pr19cYIBk2qtwexsmWeeOkU0ZhMMWVy6lGdgsGWRmMJSwteCGO/Zp07y1G9PEIxY6j3jOe6+ewMffTBOueTwyHd2oiH47pAgmrlA9t1/x4O3/Zh8ZZqu3s00Gi5CBLEtHen7vP7yaebm6wSCFpGIjWUbjI/kmE2XVzykrbUQH7ME+/rh5UuS4QTc0CpWzdMFVJaJvOKhdWq2PgLrWMc6vtTIlB1OTCliGguZ7OiOLktqWoKuCebSJQQSiVoKXqj7eJ6PlDCbLpHLVrh0qcDDj27DTITZ169hXLY715f86ryk4UPEkDzYB1FLAwQPbxC8Nu5zJifRgbm66k2bLCmy1RJYEFEswQMiCZuNgRZS+Rq5bI352SKhkMX7740xciFDIGiy78Awb75+gdvv2sDjvzqGHdBJJkN8+N44gaDJV7+2mfcOjy0uP4fDFslkkLGRLGOjWSYnCkxPF/EXfEQPbcWwdJ57+iSOs/L2u5DK89h3d4LQiK20oMQwlJdjsiXImdOz5LIVbtzVxcULWbLZKvsObFEeqEh+/Kd7KJXqVCoNzp4OLvKiYjOlaYEYplpDfPrJJG19SSYIsiGqCJb9d+rCEdzQIrG1MP/sX+5lfDzP3EyJVFuYnr44rW1h5udURTnZEqS9I8I7b49QLjuEIxYjF7JcOJdZFFJVyg6O4/Gd7+/CcX1m0yUiEYtGw+PQI9v45c+P0mh4aLryh7Vtg3vuHcQwNWzboFZT1VTT1CgXHRLJIK0dIRDJxUrdwpWYqUoi8xepv/NfSAzdihO44ZrOuD+6NL86gnBL2+/XimK5ZQqv/xSvOIdXnFP726NhJHqYmy+BZqpjRrWEtLaFIR7ll+d8drQIbutQjhbLLddEyODbP9jFL//mU5y6igAuFuo4js93f7CLUNhgrfwkH2jtCPHwY9u5eD5Dplk1jUYtPvt0ivS08s6dvpTjQE+SuFYj//pPcecuwPP/hki0FaN7B127/9+8+MklspkKhYKq2ttBi/0Hhjl5coYbbujk+ZfOAaDhY1jmikr2SEESMZe8T3vCAk9KAsZKYtrwJefzMFWGVBB2JK2mlZSg6qz3nK6T03WsYx1fKri+JF2o4/qSXMUlU24g0YgEDG7sjV1VoRyNqXYOremRKFFxmQs10EjEZmK8QC5b5Zd/e5R9B7fSlgqihUMrKicnsqqi9/AG5Wl4+Sfe2yM4lYXjWUlQV1nmt7YrE/8nL/pcLMBwQr1WBypll4lLeWpVh/aOGO0dIWbTFWbTZVKpMIODKWJxm7ffHKGQr7Hzpi72PbSFctnB83wGN7UQidq88uK5FT2eoZBJKGIxPlFgy9Z2SqVZZTUFOHWXt9+8iGVuYrbk4UtlGWQsGz/H8ZieLnLajPORr2I1N8SgNSDIThZItgTp6orx4P4hZtNlXNfjJ//sVrLZCnNzFdrbI7R3RnAcl9a2MPWai9HMNvc9n2DQRDdUDKhlabR3RPjs02mOHEvzRz/ciRX8fCuEmYrPSAH2dl6ZwRqawAOCEYPBTUk2bU6Rnilx/swc8USAnt44gYCO0OD9dy8xPpZj/4FhHMdvJkEt71M02HdgmFyuSr3aINkSIpupEo3Z+NJn/8FharUGhXydnt4YjYbPm29cRAg48PBWSs0Eqrb2EO3Nqpy3MBmbaPiSw9PQY5aJHv5PaEJQeP2nxL7xb3GMNRRP/1DIrPQoBdB0uPOeDTz31Ekcz0cIDQHYAZ177tlAT1Twk9TStVhoGYhZMFaUvDMtuac7zP/wP97GxKU88/MVUqkQPb1xQpG1iekiBIyOZPns1CxWwCI9VVDpabnaYmjGyIUs9ZNp7rqrn9a7fkRk9zdX7CJqTnHosR2cO5shl60RTyi3iLNn0mzbpgSG6qM8/HKWBskVQq+YDU9ckNzZKck5gmxNfXBfROL6YGjKmuv9GcmmuGA4qXp4dU1gGRpOw2e26HByqkQ8aNCdCHA9Yp2cwor853Vc31ifC3/4GJuvMpZZsE4SSDTiIZMdPVcnpp4v1RJd06ZJrdDKxZtaIGDQ1hbmzdcv4PuSWqVBpVjnzc+mOfDIVswmUXJ8yUdpyR81l/rX6nfUhGB709D8cjzQJ/jFWSWIGikKOmplfvXzT6nWGhx8eCs/+4uPGdqS4rbbB/js03OYlsbZM7OUSw4tKWXj89orF6jXXSIRi7aOMLfs7uVXPz+6mBcPKsv94CPbePONC9iWruySsrVmcpNafrYMjbl0adH6Zy0/1Eq+ysN7lKVRuioZKcB4GRrjZTb6ElPXsAMmyZYgxUKdTLZCW1uY1rYQZ07NUi7V6OyO8/NffEqqNcjd927kmd+dwvN8kskgtZqL73k8+vUdZHMVcvkq0vGYnS7SO3j1ZKuzeclbkwvm9J8PHwgEDSqlhqqEhkx0XS3DNxo+U5N5QkGLR79xA+VSnc+OTvLQwWEKzaCBeFxFXX784Tg37+7BRz0A1OsuuayK2qyUHD79ZIpYzGZmpsgNN3ay6+YufA8cx2V4axtSSAIBE9PWFh96PCn5MA2VhqTYgK1JwTZnhPTYEQBqo0eIZS5C+7VVT/+uWKiaLocebWVqzuezI5/w0IEbKFUlhZKK2I1EbY4fm8YO6ItG+ABtQdXD2vAlR+clu1KCNyd9bkjp7NzexrCm2hB83786MWVJgZ8vOgRKDq7jks8vEVNQFfgPP5glm63y3R/cvPi9XY4IsGNXgGymQma+gh60uO2uQV5+8TSVYh1NaOA6uKV5TDuMg74o9Gqx4bFBODqnlvIX4mBPZSHnSFwJCUvwtT6xwq4LYLA9zMmJAgAzhQYzBYeAqdFyWTLW9YDrnpzats3GjRv/ex/GOv4RYH0ufDmwYMPio9GTDFL0Dc6XTU6dlxiasm3J1qE9qG5mnSFBwlaVjUjU4sChrTz79CnqdVfdQAUEbIOHDg5TKtXJLXiGSphvpjWlm0Sp4Pg8PyZ5qH8tL1SFqiuvGjWplv6kMvVu8/iL//optVqDaMxWBvE1l2NHZ3hw3xCPfesGxkaz5LJVIhGbRDKAYWrc85WNTE4UiMUDDGxI8unH49z31U3U6i7ZjOpd3DjYwnuHRynk6uw7MMy774yCCqjB83x0XaNYVhGUVReCulLqX47llkbtQUFbUDJWgnJLkDfeucS++weZmiygGwK/aQ3lOB6VskOx1CAWD+J5Ht/9wS7Gx3LU6y5//Kd7KBRqzM6USLaE6OqOUirVqVc98tkavu8zn6nSv+nKyVYNX/LWpOoHNq4hPUpVuFV0aKPhUyzU+ezoNMmk6u2dm6tgmhp77xzgnbcuctMtPdx+5wC/++0JTEPD8yVnTqkl/X0Hhjl7aoaDD2/j1ZfO8dUHN5NxK7x7eIx779vIoUe2kslUqFRcLEunrT2Crqsl/pdePEs2U10hfLpUVg8825LKcaAtCJZbofD4T9WRN4MV8q//FPOR/x09EF58KPKlbF63v+cH7zWqpnooQS5XJ5PO8fqTbxGJhYh2byCdLi3aSV2eSNYRhI/TStneEYKyC9/ZLNCFIqRSqnknhPhcEZyU6gEzFjSolup4vlxBTAMBg1DYJJ+voQlBeqZE78bEqv36gG5rtHVHiXdGOToP42cmmZp3kAhCuofZtM7yirPoyZ4V59ViC+7rUeO904eXxyWGJjg4cPVr0BI22dIV5fRUEQ2JrkHQvD59t697crqOdazjywVFlATtMZtN7WF+dtrn+0OKoBzLSIbi6kY+W1XJLXVP/f8HFyQbY4LhjjCHHtlKoVAnl1GelOGIhR0wePXl8yt0TvF4gIvn5pnPVEkNJHlhTHJXp2gKStbGz89KfrKtuYO1jh+JEPDIRp1LZ7LUaqrvLhyyyOeWrHlef/U8t90+QFtbiI6OCHOzZfK5GoGgyckT01QrLqGwyYaNCXoHWshlqvi+z44bOwmHTcYv5ejpjbP3jgGe+M0x0uml5X7Z7G0sF+t0dkSxNWUMrmmC5bx6wdJosuRzKispNzUdfRHo747y3psORz+d4qabu5mZKoDQqFUaTNWKnD4xg25oFIs19uzp46XnT3PTzT0Egibnz88Tj1rcuKuLSqVOJlPBtg0++nAcw9AolhpEwlazYWJtwuI0leiX9wOvhVxd8uqE5BuDqp9DaOD5Pjfd0s38XIVOYgwNtxGLBWi4Hl99YIjfPXGczq4o3/nBLqanihTydSIRi3jcxnE8Htg3TLXmUKk4vPXmBe6+d5Dh4Tae+M0Jbr+zH9M0MAwPIVQ7iVN3eeb5s8zOqWvs4fLc0yd56Fu7OJEXPDRw2QNP5iKFC59QcdVM8iSIM5/QNTfCa3I7eUdSaArGGh4MJSW3tv/9WOStVTUF8Co5Eoklp5NSoUKldhE92cNCB/fl/qwxCwoNpWIvNeD+pkH91RwirtaRaQd1HnlkK7/55WfUa0sio4V2i3ffGQVU7Gk2W6V/MHHFBxwpJZeKkq6gwO0M8oFULgamdHBrFXQNfKeK7jpr+M4qJGylzN/YFN9/Miu5McWqB6aKK3lmVLLJcprhyJI9A3GCXyTF40uE656cOo5DOp2mvb0dy7r+SufrWML6XPhyYGHl3mveKYYSgsky9EdVnvcClpuR90WUKKToSM7mAyRbdCJ6iXDUxnU8dE3w6svnVxC4QMAgFDLJF2qEwxbvTkse6BUkrhJVOl2RKCHulV+jIWgPSExDMN/0fAQoVxziiSV5/ycfTXH7nQO0pMIUizU2DaVwXcn0VIGNgyna2sKkUiFeekEZw7e1hdl/cJhI2MQwdSzL4NhnE8QTQaRUrQb+igqSoLU1RK3e4J57N/D8s2cIBIxFtrdQ2bODOpQlOQf29QnC5gK50Pn6o9t47plTvDRbYs+tfRiGwA4EKRRqbNrSimFodPfEcV2Pu78ySLFQp1So0tkRIRoL4NRdTMvAcnxKxTpjIzli8QCWqRON2pi6oH4FphI2BALJZFnFwF4NPqrP2PEk0oNqpYFtG8xMl9AEBMMWmiao1RrE4gFeeP402WyN6ekSt93eT1tbhFDIolZr4PkQiQZ46YUzZDJVDj6ylWd+d4q33rzIAw9u5pHHtjMzU6RardPeHiWRDJDLVjj8zhiFbA1LVw9YJQdKjkd6usjXtiRWHK/llpl/9adUXUhYLE16JBz+Kfu//r9zshym1JAcm1etIudyze2/RwV1uqy8fD2p7JAe6vQJ7/kmkd3fpOpB3ZUsyJraUwME2uu4jSViuPAIsZY/qyYEli7Z1yd4cxLenpIMJQR6scILT5/GaSxziDBX22itdS1THWG+9d2dTE8VGb+UX2y3ePed0cXvsGFoJK9AKJdjMCbwfMlcMkzMBgsfP6eqpqpiLdGdHJ0doTUru+9MS3xfJUMBzNUkUxVB32XGCm9NqpYGvwolBBqSgHn9+m1f9+RUSonjOF8ovm8dX06sz4UvB1rCJjMFh1ylQbnucmv7lSsPnlR+jwLojUDUEtzSBq4LYw2Xit1Cr3R56jefMTe7RBQDAYP9B4Z5990xTNsgHAtwU6tQROGKUPGS39r0+eTA0CBTlaRal1hVcY1s8ReePcOD+7cgpfLU9DyPjs4o3T1RTNNgfr5MT2+crdvbae+IYlk6hiEYuZghErUplxyOfDzBfQ9s4vWXz1OtNvA8iaYLIlGLe+7bxHuHxwCaZuQOCGi5rIp1eFoZuIeX9dD5QKo9yLe/v4uxi1nm58rE4jYtqTCnT84gJey6uYdzZ+ao1V2272hnYGOC+dkyxaJDo+ERCVu8+84oHV0x3js8SiIRoOH6yiar6lCveGjmldOtukKCsaJa1u4MrR73hVCBmgs1V/L6pKThS76asjj+2RRbt3Uwmy5RrTQQlk44YvP6K+eVo4EH+w8MIwT81//0AanWIIlkiMx8hclm32AwaBAKmtxz3yCGLjh5PE17e4TNQyl836dW9/jwg0u89foImiZItgQxTV1F3zbnUqNUwzQ0XE8upRvNX2Tu3CfEVxBTAKF6T7MjbGm/gckynMvLKyZDAVwqqaXv5Qr+y3E8q8jn7R3QHdE4kY1wzLsHQ8CmpGAoofpCp8qqgnvnw1FeePoUeB4gCIjmw8zBrWtWPlMBmCxLvtIjGCmC0fD41W9PYXoerusthmFIX64QH10J2ZoHpoFp6aRnipw5nV7Rb60JQThq0d5xZb/cD2Yk42VJrq4qn7e2wmOPDvPK08coO5VmLCoEAhb7HuxDr4xBaPPi+11f8pen1b5/uEVQdSWHpyXjJbi98/JPk8zVYF+/oBwMki7UVXuNhKt0AH2pcd2T03WsYx1fLrRFLS7OVak1fD4eyRGyDXb1xdCWZRn6TXHJeEkyGBM4vqrYdIcl9zZ7xcK6R0/U40zJ5rFv30h6psil0RyxWIBAyOT9d8coFersPzBMteHTZcPVnmvKrhJ/LPcCBbWMP1OGrjAsVLX2dAieGfH4zjLPR2im5hwa5rmnlal+Pl/nyEcT7L61j3Nn5/B9STIZpK09wsR4Hrfh0ZIKYZo62UyFZEuQjz6coq8vSXunIqvZTJXPPp3invsGqVQc8rkakajNwIYkv3vhPPViFUsTvPXGRcIRi/u+Okh7Twzf9xdJhuMrYn85fCAQ0km1hrhwfo629ggvPn+aG3Z2I33J+FiWzVtaqdVcZqZLZDMVevsSuK5kfrZMNlNl9619ZLMVbt7dQzIZpFh0+PjDcQ4+so0P3h/jplt6rkhU9nTAaEHwYVoikJQa8NVeQUdQUK/5pKeLZOarJFsC7E1GGGw18BCMXyzS05vgpedPc+vtA6RSIRquz2y6TE9fgsHNqaZYSuPwW6N85wc7+c0vPmP8UmHxs4NBg+98fxevvHSOkYtZ/umf7Gb0Ypaj01MUS3XefP0id929gVBQsVDfl2QzSjSlNZd8pZSEwibHPpkmHLHo6IwSsR2mX/kpEZMrCvwWlPvd4RDlxpXnJKjK6+uTkrGS5O6utSt1u9tUX/bC/NyeFGyJK2P5yTL89oIkasFARNASADMQ4sc/2sXUZFFZaUVtWjui1GyTeQeSyx7iHE+SqUGmpr4Dm+Ia4xfy6A2XbL62qsoaiwcWe7xXE0vJJ7NwOifZljQYaAlyy55enn3q1GL3h6YJOroiHHp421VbBG7tENy6rG/X94Caw9fvDzKRHiKXd2hJWCSDVfxzj+NXNxFu6aOmqbaGExmlwv/ekOB0Ti3nb4qJZelQS5gsQ7Ipyi/XVaXY1K+tV/rLinVyuo51rONLBU0IhjvDnJkuU234VOou+apLKrJ0R3R8COjw9UGxeIO/rQOOzsEbE7AnpZTpuibYkZRMZXVGRnJs395OPldlarrMrpu6CUYsPj4+yzcObeHlSx5f7dWWfYZc0SOYqy8t7a2AhOfGJLd1CHa0qGSjS0XY2yE4WjH4TtPzsVZrMJsu8/67Yxx4eCuWpTM5kce2TQrFGgMDSQrFOumpIoap0dUdU32o+RqhsFDG+tUGF85l2HFDF8GQWiJ97ulTZDPVRfJpWzq9fQmefuk8+VyVhg+pZhthueQwPVWkoye64hS+N7T2TTRfl8zUJIGqS//GFG+/NcLe2/vxfUml7NDWEcYO6ASDBsVCle7eBNNTRcJhm0jMplppMD9fprUtQlt7mDdfv8jJ42keOjRMOGwyM11aVEmvGbeKYEMMNjRjS9NVyWRFoOUr/O7x41QqDQxDw7R0pGnAQ8M04hEy43mmJwvc99UhZmdLlIs1unrixJv9lMGgieO4vPryeQr5Gttv7OBP/8VexseVOXuqJUSqLcxbr19g5GKWhw4OUyzUOX16FunD0JZWymWHdLrMho3JxWq470scxyVgG7iepFpr4DZ83njtAqapY9oGB/YN4BTzBK6y4rtcub81qWyLbutYfY2UJZUat9ECuJ7PvT1iFekNmzBWhGMZn3R16SFsY0yR1u8PrXyPBsynHY5+PIFuaIxezDCfH8G0dL6yfytzsRBDcSVCe3VCJSi1hQSvjEse6IP5+SqFBWLa7H9WbQOSfL52RTFctq6WzV0f4pZPKhYkErX48T/bw8x0iVKxTqo9QrwjSgGNTydUtfbzoAmBBIz8JQq/+X9hBtoYTiSw9K14g3/MpDHEubkarWeK9PTpNCyDj2YlnWH45Tn1UNQWhKoHR+clCQtsQ1VFfQmfzkkGoktEWM1ddd7Xq4PMOjldxzrW8aVDMmRyQ0+ED0YKSCARMldsD+iCna2Xv0v97Xxe8uEslHOSO6KSVFjQlTRo3NjNay+ewrIMdNtgZLIInsdDh7ZS1HSKy6JuzuUls1W4o7l8l68rtfVaBEETggd64c1Jyanm8uktrYIjc5L+GARbQvzJ/3AbE+N5ZtMl2joitLaGCQR0LEtnbrZMueQQCJicPzdHuehw5uwc93xlIyMXMxQKdUrFOjtv6ubMqRnuuW9Q2fLIhWX3ncxMF8llqyRaQsSiNq++co5sporrr+5QXEv4sdwuy5eSE1m4WJDELUHIkLQlghz/eJy9t/dTKtWpVVzaO8JIHw6/OcrOm7poNCRjI1n6+uI4DR+n4GLbOvFECMMQnD87R0uL6ptt74gwP1emWFCBBAOCK4palqMrKAjVHP6fP/94UWi2cA1aWoK88+JpfvhPb+Z8a5jXXj5PJlPlnq9sJBYPMXoxQzBssXFjC5cuZRkdyXHjzi6CIZPXX73Anlt7ScQDxGIBcpkKn306RU9vgi1b27EDBu+/O0YobHLb3n4++WgC35PE4wE++uDSYkpWve7iuj6eIanVGjz4tSHefWeUQqGOQBCN2zz76/f5xv0/wXn+31z1XBeqp3s7Qvz5KclADDouezgyNcG+/qW/nchIfnlOpUUNxaEhYb4Gx+YVebqzc2UFdS24vrKP+uXjJ2k0PEKGap0oNaBV93n/pVM8+M1dfJDWmCip6FEfdWy72+C9GUlnxFIkrenusHhpJfi+RzhsNefcyote91R7QrkheXUCErYkEdBpCYVp7w4jpcD1PE5lJVMlyR1d1078LL9K/o3/hBDQKMxhDGwnP/yn/OK/HqXmLPkiB4MW3/2jm9jfH0YTqqIcNgRlV1L3IFuDTB1KJah7kogp6I0Itjf74SMBRcscT1KoucSD5lWO6suL656cGoZBe3s7hnHdD8V1j/W58OVCvqr6MgOmjv4Flsc2xQUbowaZZIIPZjV2CUlXSGAmQ+z75i7q2RLZTJVIIoAbiVCwDPqNJaP9j2d9TmTA1uEvTks8HyxNGe+rvkfJ+zNwS9uSYrc/KvjukFIsL1SgNscFc/N1pi8U8TyfIx9N0r8hiWXp/If/+zAAvX0xdtzYSXd3jImJAqahMzTcRndPjFKpTiQaoLMrRjxuU6977Lixi+NHJzn46I7FapQZVL6TA80EIqfiKp9PU5BzFHlYgGFpJNujV+3LPqKCgjjQv7AsKdDQuP3ODTz39ClMSycQMDjy6SS1SoN9B4Z5642LDA23Ua95nDyRpqc3zsZNKfK5CoGAztRkESE02toDtLaG+eTjcVKtYfK5OsmWzxe1LKBW9Th7epZatUlMm/zGl5JMpkqrpjE7U6KzK0ogYJCeKfHbXx+jf0OSvXf08+pL54lETG7Z00t6psjZM0oYU6k0sC2de+8fZH6uSr3uEU8ESSaDhMMms3Nl9uztI9US4vFfH6NSaWA1vWUvXshSLjfYd2ALlXIDBCQSAbRmK8Vss9fZ9SWlQg0zESdr9rHhu/8/pFu/wpmqc7M0H0cI7umGNyYk39l89e/B9hZlCn8mJzmWUeQ1aMC3NonFON2rIV9XD3YbakUs30OTPtWKj24IooaOBBzHp54pUjDjJG3I16GqXKZoDQpCpqDgB7Atg0rFWdUqY9sGoZBFo+4hLvMJjZjwzpTk3m6BJ+HVcck3Ngl832d56NJwQjCc+GIVST03SuPSUYQQmLFWuPP/wy/+y2dUnZVBHZWyw89/9gn//H/cSzhqLrYNhA1B2IAWe/leL2vxkZKTk0UEEksXhK5TpT6sk1N0XScSubY84nV8ubE+F/7wIaVktuSQLbuki3VAELZ1xkvK5uhab0iaptEaDfJAWPLquCTcKYhLj7HpIn7NIdUZIZEM8VpaUCn6OK6qMOXqPp/OwQ+GBAFD4CPRLrsBfTILhibx5eoq1nLUqx652RKvv3qBXTd1MzKSJRKzm+GqCpMTRcYvFXieM3T3xOjti9PRGeHxX33G3jsGuDSaxTA1qtUG6ZkSjYbPD39081LqUBOe9HnpkiRqaexsNXnoYZVRbnueIstNQctX9m+lIHSuJn5fKwpzSRzVrNLmauy4oZNazVUK6pnSoqPAA/uHeP21C9x51wCGofObX3xMvJkpf+pkmktjOfYfHG5WIg3aO68sarn8ms6lC2QzlSX5+LK3+b7EabhMXCrQ0x/juz/YxS/+5ii1WoOxkRzlksMddw0QCKiI0YcObSWXq5LNVGlJhQiHTVzXp6MzQj5fo1h08H0f2zYZHm6lWKzz619+huf6eJ5cYWuUninxu9+eoKMryne+t5OZmRKvv3yOTLaqyDPKTcCX0PAE2YpB//Y7Fh0proQFbwlNqIela4EuBNuSgm3Ja3v9csRtwUP9gg8Oq35h5VOqNFuaphGNKx/esZkq23YmOTzlUWqotoEFhE0oSp99B4d55smT1J2VdlD7DwxTKtSYEZKu/vjitV+wA9vTIRajQ21DMleVi//+faGss/4TZvtmtN0/xqSV8VmfmgO+t3waqfDjWrXB+FiOzVtaVxHoq6HqeNQcDw3Jjb0xzDVilq8XXPfk1HVdyuUy4XB4vWJ2nWN9Lvxh43y6wni2xlLHlsA0dIY7I7jAOxcktrbUf3g1uK7HidkqbbEg/TGN+XSFd148RanqIVHLhwFL52uHttLTF+KvT/skbXh9XPWyNlfmVhHTD9KSiZKy5ElYMBhf+/OFEMw1BUGGoZHPK+/LzHyFzUOtzdesfM/kRIHJiQK37u1jz219HH57lN17eqhUGuTzNTYOptg42EKyJbCCmAKYCO5OeoxfynLkdJne3jjf+PZOZtIlMrNlWlqCJJJBpmbKRGt1GtfgN3k5fJaqtBs0QbXU4PzZeW7c2bUiXemVF89RLNRIJINkMlW+um+IhuNxaSxH/0CS4W3tvHd4lHK5wff/6CbswLUdh67BbLq0wo7rcrgNn0jUQkjBp0cm+dFPbmE2XSaXqdDaEaGjI8rURA4hNAr5GmMjWYaG2/j4o3HwJTfv7qFSbqDrglDQIBqz0Q2BU/cp5OvcvLuHYNCkqzvGk48fXxElGwwaHDy0lTdeO8/eOwaoVFR115M0TejV6zxXXrFavBAkcDmipvLnVIb8/7A9jPWqh9AEjcbKPCfP8yjmq8SSITpbg7w/7dPw4QdD8FdnwPF8LF1DSkkqFeKdt0bYf1BVk5fPjyOfTHDrbf3MzZbpGYgvtnOcykr6I9AfWTq/vR2CI7OSobikP/b7Ez1L84nc+SNm/X6ee+Y0/Rs1NKuM7y8Q05X7lhLlj9tVJ5YKrnh48pGcz0G5oX4nNsZgtAhbEgK7abgvUZXy6xnX/R3Y8zzm5+cJBALrhOQ6x/pc+MPGeLaG37Svbm16YHbGAxi6hoGKFPybs5IfXwM59aWHWyky6pt02jrPP32KAD6B5hp3UAeJxzNPneJ/+ONdgODrg+oG9ddnfNbqybtQkBybl9zdpVoH1iIJRUdyKgc7UhqFfJ1crkql7BCPK0I1OVGgrS3crN65qwhqMGQifYll6+y5rbdpDeXTP5AgEDAwTQ3NEItkruBIZmuCaKm8KLpagG0b/JM/3k0oYPDMU6eYnS0jhCCRCBAOmZ/rN3klSClxPYlpaiQSQQ6/NYqmC86ccigU6osq92KhxltvXuT+r27m008m2XVzN8V8jZnpIjff0sOmoVZiCetzIy0XoPxHbep1VwmQlhm0LyAcNolGbarVBoObUvzml59h2watrWHKFQfb0onGgly8MI9lGXR1x3j+mdMU8jUe+fp2Gg2fp544yeCmJLtv7SebqZHLVejojGLZOsVCjWgswLtvX+TgI9tIz5TIzFdItYbo6YnzycfjbNiYWrT88qoriSlAKGyQaIvwUdqn1LxcAV0RU1uDm9aoXLcFBR0hOJuTDCeXthcdRYAWnBYavmS+trbtFijrtarLqtjNBQghGB8vYJr6CsuzBTQaPpYOejKKUZR8b1AgEOxK+UyVYaBpVG8HDe68awO//PlRLFsnHLI4d24Ox/F46MAwx49Nc8uenhUEfa4Gd1/WQ5oKCHak4MO0pC+2NnG/FpS0KE7yJn73Vx/jEyBfhqGeq6+yJVtCTE+XaGkN0ljWNPvWpHpYaA+ploYXLkmipiKnhiaIhUyKFYfR+SrJ0PXZbwrr5HQd61jHlwSGLnA82NwRoTsRWLU9U4Pu0LXtSxOCpC0Y8QRurkSl7iFMsC9bencbHmMTRW5sTSx7L2ss50s+nZXc3ApDiStXcMImRAzwGz6GoRGPBylc5m/65usXmkvOn66oTgVDJt/5/k5effU82UyV2+8YIJUK03A9wmGTVGt4VbUzbILhuPznv/6Uaq2x2N+niaYyf7rAB+9eIp0uowlFLLO5Gpomrslv8qpjbAgSyQDf/M6NzMwUmZ+rkEqFaG2P4Lou7783rsRDx2bYcUMHufkKdcdj244O4gmbQMC4QjbU2vB9n56+OL/91TEeOqjsuJaTp2DQ4NBjO3j1pbMcfGQbb/zmAt/89o1cvJChWKyTSAYJhSxOn5qhqydBMV9jeqrIPV/ZSKo1zOjFDIGgwU/++a2cPTPHqVNpNmxIkmoN8/abI2TnK+TzNfoH8jzy9R2MXMxg6Bqbt6TQNI3R0SzJljCffjJBX3+c+x8c4vlnTuM4y65x0OShR3ZwqqLTHZbELZX+VPNhtqIq+ldCypa8n4bBuFxsISk04Hwe7u1Wr5ksqwpkZ//aJK7gwNMjKjP+1jXEfYaukZ4p8tmn0ytEXgsIBAwe2LcFLWjwcGrp6gVNgbbsa+FJSVu7SmrL52rkcjUSiQCRmM2JYzOUS84qM/9DG9Y+5s6QoD+inDh2rRJBXhuEEMxMF3EcD8MwmE2XueuejQSDpvL+vQzBoEFbW5jRkSyVsrv4HRkrqgr2zc0HiJ4wi0KoBbRHbQqVBg33iz72fbmwTk7XsY51fCkQCxjMlRpM5J01yelsjavEiq5OzgmZcEtc59gRtaReboC9TMygaj6S+fkqG3uTsKyhYDkxna1KTmYlIRNubL165eajNAhNkJ0uEGvGYNq2wXuHxxb9TU+dVCKcH/14N/Pzauk/0fQ2/ej9Mc6cnMWydDRNcPp0mptv6V7lS7oAU9cZHc9QrzdoapfUaPiSWMymVFDxm6ap4Xu+isn0JY7joeviqjZOnwcfCEVNdEMj5QaJRG2EAOkri4D9B4bRdYEImMhKnUhYo609xLHPppgcL6zInr/W23gobPDVrw3xyotn2X9gC+XmknEyGWBoSxsffXCJixeyFAo1HvjaEM8+fYrb7xzg/Ll5xi/l+OSjCe69bxC34VGpNOjojKIbGq++eBaE4L6vbuJ3jx8nm60SCluUijU2bW7llt09TE8VCQZNIlGbatWhvT3CKy+dw3G8RWutRsPjuz+8Cd/3ef/dSzywb4hqpUGp5JBsCbJxU4qcZbF7mScuBsRZrcS/HD0RQXd5oTLa/FtYEabRolLjR0yavqhrJ0klbcGjG+DFcclsTbIpvlJYJIQkGrVJp0u8+87ooshr+bK86/kMxQXOUjmRkqOM+BegAaVyA9+HI0emMA2NM2dmKRcdWtvDPPzo9lUPWleriu5shRcvSSZKahw+D76UPDemwgluaIHBhE42W13xmiMfT/DdH+zib352ZMWKw0Ll/913Rtk42MKl0Rybt7dyOuMzUpDc3X31z89VGkoQZVy/YihYJ6frWMc6viTQBLg+vH7J5dYBFRVqCBbFEBfyck2xDijD7LN5n4AO3WGB4Uu0Omw2fbragmhNq6LLK6KmBpFEkIarbrQSlUa0gFxd8sKY5OENgvhVYk0XsD0FtqFx9HyR1tYQobDJgYe38uxTpzj89ugioSqXHbLZKuGwRb3u0j+QYGamRChs88C+IWzb4N23R/B8SVdXdJUvKaglXV9jRUQqKNENEkJhi3y+iuv6mIZG3VtssGumFUEuW2VArPabvFYs9KG2hqKqMtsk/L4EISX1qofte4xO54iGTaTvUqsohbrjeF+4eusBnd1hvv7tG5i4lKdWc9ky3Eo0FuCVl84uEv/XXj7Pw49u4+Aj2ygWanzlq5sol+oUi+qz29oj9PTFmZ8tMzlRYNctPbR3RnAbHrtu6aFccsjna7S2hmjviPDxh+PKZ7PsLBL/hx/dxr6Hhkmn1dJ+SypEW3sYy9Z4961Rbtmj+lcdx6W3L0ZXX5KPixp3J3+/CFJQVk0rjd0Fu9slL4+r5KKYpS7xdFnSeYUHuZgt+NYmQcWVfDIr+WBGsqtV0BuBlIZKDrMN0ukSv3viBLGYTShkceaUg+N4/PGf7sFdVuH1pWSmKrkhtfR59arH7357gnDE4p57N1Iq1hcJbiweIJ6wv2A7ieDmVnhhXPLYRqWcvxo0ITg4IMjXJZ/OS45mfAbDQSouuK7E1OHI6Qx22OKf/Y+3MTGWZz5TWfS3fe+dUQY3pzj89ig7dnZSmYdaA+7vvbKx/vJ+YIkgW3HxfPmFnEa+TLjuyammaYRCITTt+lXFrUNhfS78YcOXKvaz3PSMGS1CwobWpv6l3FARpWthe4vyGay6Kq6wVBfMNCwujUv2tEcwTR3P8XA81d+3AMPSibZGqLiKMOTrS/2Bri95bUJy6BqJKUDMEBRyVSIRi3feHlUJQmGTP/rjm5mbrVAs1ujpidGSCnP+3Bz5XJUNgy3kclV6emK89vJ5hBBk5svU654SMrWE1hTPBAx4e9qnr3Wl9l4TIAWLva6GoS32Zy4I3E1DUGzINT1Pfx9IKZsEd0leMp+u8twzpygVahQyyq82YBs89OiNnDgxSzZTxXG8a6reHs9IzuYkQii7IYFGd1cbW3vqPPn4CUYuZJbGJWBwy55ePvpwnJtu7qXaFCZZts6WnjY0odT1uWyNtvYwN+/uoVZrUCrU+fijcbYMtzX9WZUyPRgwGd7WQao1TCFXI54M0j+Q4KMPx+nsiqMJSKZCqkosBGOjOSYmihz5ZIpw2CIUthi5mOFO2+DOjYm/0zivRYwsTXCgH+aqUGyo1peRInRezZIBCBmCu7oEN7RIpqvw6RwgfPbGLQ4cUg9U9bpLIV+nkK8TCBgceHgrkaiF7y9Ry/dmJL0RCDYJ4/IldGdZOEQwaJJOlyiXHB6wNq9xza9O2luDgnu64NfnJQf71b8dT1JwuKKaP24L7m1WOp1KlI/DBq6rvuMCwenjM0xczHLbHf1saW3l4kiWQqHOwMYWDr89yvycCmToiAki+tV+A9QDwtf6BI7nI75Qw8qXE9c9OTVNk87OVUG367gOsT4X/nAxV3LIlBsIIUgGNeaqkq6QMsPfHFfE1PEXxCVi0ZbnckITNJSvI2GToZYWJsqSDzKw79BWnn/6FFXHw9JVhcO2dB7YvxU9aDJdai7paxrbUwLTgLPzkh0pScRcFr+oaeiaEucsv0EvoFJ2+fUvjnHo0W1Uyo6q4D22nenpAuGwRUencpIYH8sSiQaIddsUCqof78jHE0xPFYkva2nQdI3OzrV9SU1N0GJLOrrjBALmiqVJhKBQqBOJWYRCFqWSs/BnTE1gmTquptN+hX3/XVGvqqpow2ngOks9ffW6y3NPHefAozfy1hsjwLVVb4fiyqXB1lS1NluDkOkRFAb7H9rChQsZspnq4tLzRx+M88DXhnj9tXNMTRTp7U+wc2cXf/OXn1Cvu2zYkCQUtjhxfIabbu7GDhicODbD/Q9sZnq6CAI6OyLYAYOZmQKxeAhNhNGEQNMEuWyVvoEWioUalUqDttYwrW0Rnn3qBNt3dFIpK3/PQqFOoVAnGrMp5WuYuvhc+6jPQ8NXhCwVWFlBbQ2qB7mNMXj8gqThy2vyNk3agpDvEc4WyWaq1AxlZ3bg4a2UinVy+RqJeIBI1Ka9I4Id0FeI2OZryt93U1wSNdV3M5tZuYReLjmUS0vzYK1r/uYUtAUkW5NXPua+iOCbg/D8mKTqysX+z9YrGzgsIhAyePjRG3ju6VM4joeUkobjUzNcKpUGh98ZpVppUCkrYR+oPvDunhhhU7LG130ZBDVP4viSSrNxOBr4Yv7MXzb83uT03/7bf8uf//mfc/r0aQDef/99/t2/+3ecPXuWRCLBt7/9bf6n/+l/+ns70H8oSCnxfR9N067bmLB1KKzPhT88OK5PuuhwPl1BIvARPLgpxAvjkq9vFLi+4LcXJBrwUL9Go+oxM50nm6mSbFHemWtbIkl8X9ITVsbZp7JBDn17JzJfopCpEo1ZtLVHyOdqpCdytCaD1EMRKpkKlUt5jscDtHdE8IGZCZeO1hAIGB2ZZ262TFtbmJ6+OKGwsXij1jSNiUt5ZmdL1OsNDj6yjV//7VFeevEMt+zu5cSxGe7+ygYsyyCeDFHI1yiVHDo7ozj1Bm+9MYoQYBgajYZPIGBw8OGthCMG9cvEFa4v+SANe9phrmbwre/vWqXWj0Qt2tsifOWrm3jy8ePU6+5itnkgaLBv/1bKuo7F3y+WV86E18DQNRAaSIlEUq86lIo1whGLcsm5avX2g7RSl9/esXIpt6PZc6mhggVaW8OEwxa5TIVqBXbf1oemwe13buBv/+oIJ47NMDdb5iv3b0JKFaEZDlts295BuVzn7TdH2HNrL3OzZc6cTHPjrm5qtQbZXJWWliDzmSoT00WGNiR48bkzpGfLxGI2miaIxWy6uqL89tef4bmSYNCkUKijaUL14DZjdJO/Z5X6vRlJvq6SiPqiqnL8YVqy/wqiJxBsiMJECTbErr5vDRiZqPDMU6dwGx42ksJzZ9m+o41779/E7EyJQNAgGgvQ0REhFDFWuSvELNjdLnhjUjlZxCxBsuXqbHGta35DC/z2gqQjpAjzlRAxBft6JR6CxDWuaIBq6Um2Bfj293cyMV7g0miWRLMK7jZ8piYK1JY5QARDJt/53k6KpTofV0y2J+VVj6s9KMjWaT58SEq1a/Wh+HLi9yKnJ0+e5Iknnlj89+TkJP/iX/wL/uf/+X/m29/+NidPnuRP/uRP6Onp4bHHHvt7O9h/CDiOw8TEBD09Pdi2/flvWMeXFutz4Q8LDc/n/ZG86n9EQ9c1btuYIGjqtEQlr034HBzQGIyr1KJGrsKvXmiqn5v3CMvSeejgclGNihh8f7JBJjOPHU8x1GITMHxGHYMdYZszJ9JEoyn+n//4PvWai2XrRKM2tarLg/uHOH5smumpEratljHDYZNnnz7F8NZ23nlnlLl0GSEgGDD57g930dkdwZUSfVn/5/vvjnHfVzfxx3+6h5GLWep1l9239jI1WSKTqTC0uYX+gSS1WoNf/M2neJ7kwMPD6LpGPl9D+hCOWETjNo01KjaGBh1BeHZUsiUp2NwW4k//+a1MTuSVtVFbmJ7eOIGAjut7/OCf3EQ+V6NUdmhJBonFA0zOlKhVHMLdUYIhZep/JZ/NL4KFypmUPk5DQ5gG6M2lTinBb5DLlAgFdRoNnY6uK1VvJZviqkr6q/OSrw8qI/qat0RUfSDREqBedSmXlStCLB7Eqbu89cYIui740Y93MzdXJpet4vuS/oEEHY7LbLrC2bNzBGyDvXf2Y5o6H34wztREgVMn50imgnzjWzdgmgKz5AKSSqXBI9/YQbFQ59JYlmDQIhgyefP1C5RKDt/6zo0898zppTNoGtiHItYqdfq1Yncb1DxBpq7ET5dKkl2pK7/el5Kj85KHrkhel1Cverz9wmmCeEgDMvNVPNfn6KfTnDk9xze+dQPjl/JImaOre2gVMfV9n5Kjrse93TQJqq+styx9hVPBAixLX3MskrZAF5LRgiTZ7C0fKcg1fY2j9hdv3XJdl/n5eZLRVo4emUQ3NOqOx+xMiWKxzo9+vJvZ2TKZTIWWlhCtbWE++XiCZGuEW26JUnWvfu0my5I97YJzAqRUKREVx7tuU6K+8BXyfZ//7X/73/jxj3+8+Le5uTm+/e1v84Mf/ADTNNm5cyd33nknH3744d/nsa5jHetYB6CI6bGJkiKmQme4O8runjjpsQJHPpjEnc5xU8xnugIg2B72efapUxSqHr6UOHWXSrlBqVjn2adOUq96SCSHZwRnChqGoXFLZ4yeapnxY5OEsnluj7n8/Gef0N8X58nHTyz2YYZCJvlcjXLZ4flnTnPr3n6guQT99CnK5QabNrfy7FOnuO22PhVO5EvKZYef/9URSoUGsqGWUFNtqtFv5EKWQr5OsVBj85YUXd1RDFPDsnRSLSFqjofv+8ymS/T2Jbjplm4kgmDIYHKiwNEjk2iaIGAbK9oHhBDomkAIjcG44JENGolqhf/8Xz/hP//lEU6cTFOtNThxfIZ8TpGxrp4Y7d0RtmxvY9OmFB++P87Pf3aEl589w0tPneQXf/UJ6aky1ZrPezN/D+bhEqKxAJn5KtlcTVUpIxaep4Qi6BaJZIRq1eUr928il6nSqHlr3MwELbZgU1z1Rv72guRX5yS/OCtxlq0HexKqtQbHj81wfiTP0ZOzaLrGth3tbNjYQiZToaMjwvC2dnp6Y5SKNcYvFSiX6ti2gWXpWKZOS0uIm27u5ta9fdz31UHuuHOAZ353ktGRPE/8+hgfvTdGudpA+pKjR6cY3JzCtnWmp4rs3NXNH//JHoSQ5HO15rVS/7W3hzn48DZ+31Z4QxNETEF/RI3Dphi48srEUxOCb24SvDYhma9d+Vour3AjBA3Hw3WXYjxrNZfZdJnR0Swnj6eZniyuWJHypeTlcQg1S2SKoArempK4TScG6zJituDQYF9BAHdoQDC6lGvAuzOXxYD9HaBpgmgowfhYnonxAlMTBWani8TiQU6dnOWn/+E93n93jGqlwQfvj/Gf/sN7XDg3T0sqRMiElsCVx7zUUDHBuhBs6Ywgm12nZ2bKV3zPlx1fuHL685//HNu2eeSRR/izP/szAHbu3MnOnTtXvG5qaootW7Z8oX1LKanXV2YFa5qGaZpIKXGc1X5iCxUux3FWPUkZhoGu63ieh+uuNANevt9Go7Hqcy3LQghBo9FY1Rum6zqGYay5XyEElqUWui7fJ6i+Rk3TcF0Xz1v5VLiwX9/3aTQaq967cK7/UPu92hiutd/PuzZXG8OrXZvPG8NruTZrnevn7XdhX5cfz+ftF/7xjeHfZb//vef3WmO4fL/VeoOT02XyNR9DN9jVF8Urefzm6U+pL6u0GKbGnV/bihaOkJ4pEcDD8X3ms7UVtb1S0WFivEB/f5yObIZq1WFjKsJLL5whX/NxJaQ7w4h6G6alUS43Fr0bdV3g+6oFANQNuVJuEIvZFAp1ajWXUrGuCIylU600CIVNCnk1duWSw9holkjYwrR1urpiBGyTer3B++9eYseNnbz7zhi7b+0jEDBIJIOYpsD31HFrukb/QALLNggGDUYuZOjqijG0pZXW1hDZbJU2O4iPxK0L0tPFpvVUgPbOKELAK8+dwpYeupCcPz2H3xz70ZEshx7eSjwZpK0jTK3a4Ne/OMrMdGnxfAHyuSpPP3mCb37nRjYKn3enDCKGUl8LoaHrOiDX/F6psAuB53lIqeZhvZlVruuqPcFt+JRLDrG4je9LbNtgYGOKYNjmzdcvUMjVmq4G22hpt3E9f9V+e4Lw3UFFiM7mNZ64qPHtTeB5LpomaGuLUC7VKc2UsEyN+ck84ahFMGAQCFps3pIim6nxX376Ie2dYXbcoHrTNQGxuOrxzWYqnDk1y86bu3nlxbNMThSQEoa3teN5KiFqeqKAhmRocwq3oSyzevviGIbG+++OMbiphT/645vJzFfJ56u0tITo6o6RnitRrTp090cWr8/VxlAdm0Bfts331XcjYajVAc/Tr3htggJ2t+l8mJY82OMjpc+5nCRqQUdIqN8Xw1AVbiRCQmMZMV2cG/ka4ZBFsVBndrZE/6YYUgrqTVcNU/gMRgWu2wy30DTu7dZ49ZLHvV0G3/zejWrOZpXdV2dXDDuo47iqEr0cum6QCgpc1+V8xqU/Cm2W5MiMIGIJNifNq8xDZXbvrrFfTdMxNI3MbJ23375IOBykWHII2jr1urfCg3hyokAuW130H9Z1jb6eKK7rrrg2ar8amqbj+z4jOZfBMLiuIGoLXM/H1AVSfvl4xLXiC5HTubk5/q//6//iL//yL6/6ur/8y79kbGyM73//+19k97iuy8TExIq/RSIR2tvb19wGMDg4CMDs7OyqwW5rayMajVIqlZifn1+xLRgM0tXVhZRycZtpLqUxDAwMoOs68/PzVCqVFe9NpVLE43Gq1SrpdHrFNsuy6O3tBVS7w+UXqbe3F8uyyGazFIvFFdsSiQQtLS3U63WmpqZWbNN1nYGBAQCmp6dXTZyuri6CwSCFQoFcLrdiWzQapa2tbc0xFEKwceNGANLp9Coi097eTiQSoVwurxrDUChEZ2cnvu+veW02bNigYhjn5qhWVza4L4xhpVJhdnZ2xTbbtunp6QFYc799fX2Ypkk2m6VUKq3YtjCGtVqN6enpFdtM06Svrw9QD0+Xk7LWVuXQXCgUVh1vLBajtbWVRqNx1TGcmZlZ9cXt6OggHA5TLBbJZrMrtoXDYTo6OvA8b81zXdjv7OwstVpt1fHGYjHK5TJzc3MrtgUCAbq7lbP2Wvvt7+/HMAwymQzl8sqn82QySTKZpFarMTMzs2Lb8jFca34vtETkcjkKhcKKbfF4nFQqheM4TE5OrtimaRobNmwA1h7Dzs5OQqEQ+Xyed86kqXk6lm0x3B7E8HyefkaJFDzXXby1uC68+uwJ+v7pbtJTRdyGT6FZlfKlXFTc+kjGRrLomuA3v/qMQw9v4+d/dYRGwyPZEiRkabTEbebmygRsYzFKdOG4vcuW6/L5GqGwtSiKyOVqxBMBQmGLXK5GKGQtklNQMYejIxn6+pM0Gh7f+u6N/PoXR5kYzwOS7Td0MjdbwrR0OruihEIBaq5HteLQqLu0d0QIhW2mJguEIzbBkEksFuCt1y/Q1R0j2dpOZs7hxefO4jYknq+qrslkkFv29FOvuUhfiUyklM3qlsoHz+drHPlkkn0PbWFurrKKmIJaep6eKjI3W0HT4aaUTbleYH5efZcTiSS+LxmZmqfsyhXpQ+3tHQghuZjOM5Kp4wudjYbB0Y8m2HdgycTddX3yTRJ68OGtlMp1Xn7h7OIYF4t1nnriBN/63k4cr0AsnkTXdUqlErXayu9yVzDMCcLU6nVqlRKNmsX7h0e58+6NPP/MKQrVBpoQuK5PLaixb28/zz19mrvuHuAHP7qZzHyZYqFOSypEIhGkVmtQLNU5fnSKPbf18/abFykU6ouBBrlsjUjEolisk89W2LS5hSd+c5ydN3Vxx90DzKYrZOYqBAIGQghsyyQSdkkkggSCBral8dF74wxvayOS8Klf9hvd0dHRnHd5Gg0HyzQwtRCz6QqFQp1UKkxre4hqvYDTcDk9I9mSEOTzFi0tLWoOXvbbDvB2JsUDfQbFYoF6vYZbhVfnJPd2C1LxCHokSjwZwHUVwdd1sapIGY8HOHd2FiklkYitfhPMEG+lDXaEK5ycqaBbgvnmT1soFCYajXJnh8frp+dpDUJfzKA/aeN5PmazPzyXy+F5S0Sn7oFjJyj4NkG/yumJMmeazgzjebi5Kwgk8Dx/zXPt6FAPG4VCftVvTywWx8PimadOApK+nuSi3ZmpC947PLYicEA3NOp1l0DA4NAjWwmGDWbn51fxk2g0RigUwnEcCtksMUswL6FU9yiXa6QSUTa3h5icvPSl4hHXii9ETv+P/+P/4Jvf/CabN29mfHx8zdf87Gc/4//8P/9P/uN//I+LN/trPhjDWCQlC1iw9Vlr23K0tbWtydZBEdxAYKUp98J+hRCkUim6urpW9BkubE+lUiSTyRXvVU+biuBefkzLly0WyMFax5RMJonFVnabL+x3OTlbC2spyheIdSwWIxxe6QGysN/PG8P29vYrjmE4HL7iGGqatuZ+F8aitbV1zaofKIJ7tTFca7/LxzAeXxlOvnCugUDgqvvt6upatd+FY4zFYos/2pfv1zTNq45hR0fHFccwGo0SCq2MKFoYQ13XP3d+X2kMw+Hwqh7Z5XZYa+134XxaWlpIJBJrbvu8MVxrfi/Mw0QiQTS60ltzYb+WZX3hMVzYb861CERTREyDXX0xYgGDsYslSsU6risxDIFh6FQ8tVzoNXzOnZ7Fsg2c5rKjEErFLmnaPkkIhSzyuSrdPTEqlaXKaDZTpbUtTL1cp6+7jcrHkySWZbP7vt+sTi0hHg9w5tTSj3MiEcCy9KY108ptAC2pEOfOzpFMhmk4HlNTef7oj28hM18hk1Fm7r19CaYmC0xPFknPlMnmyuza2UNbh87ht0bo6Iry2adTPPi1zbzw/BnGRvMA7LixE9MI8NLzJ2g0fASiWbHRsAMWY6NZXFdVJ11PzS+BRNc1hIB8rk6l3CCXrTI7s5qYLkBKmJsr090TpVho0NHT2nwAWPiNEEQSLZyZlZzNQX9M0N10UziRhaQZ5Yb+CJ0Rnc8+TjMzXaJQWG7iXiUeDxIKmVway2GYOrF4YJGcNho+c7NlpieL9G9MouvqcyORCOHw6u9cSwGKnkkimOCXjx+lUnGoVMbZf3ArlbLyKE0kAwxvbcP3JJMTBV59+Tz3PzCE9H0iUZVQBTAzUyQUsth5Uw8N12NsNIdlqookCOJxm3NnG3iuZGq6pER4AZNPPpqkWmmw76FhDF2jUKwpf1cNZufKnDk1yz1fGcRxXMolh2QySCy2unK6NO/iCCHJzdV5sqkoV9dTYFoaDx3aSlubRbDqEUoI4uGlpfFUanUTarCg0R4EnSiRSJgU4AQkWQG9QR0pZfNhyabheGDIRQEXKEuuYMikWHAIBE06uqLots5r0xp3dQrCRoSzlQAdbUu/VQu/W/GAyd1DKU7n4GRNMpuDtoBgbNZnMCYYCMepC8l0WdlYCUCvGmxPSvZtDiP9ABcLkhMZ+NqQINBsD9B1bc1zXUAsFudyhm3oBuMjeVXFd10iMZtg0KTR8DANk9llgQO1agPfVw944ahNNB7A9SXRaIxIZHXlFNTvYdVuYWOLIBUU5NJlImEdS9eIBgysLxmPuFZcMzk9fPgwn3zyCU899dQVX/Pv//2/59e//jV/8Rd/wfbt26/5IBYghLiiEOVq24DFpca1oOv64sBeDtu2GRoaQgixpkJ7eTX1i+x3Yd9XgmEYV8xv1zTtqu/9h9rv1cbwavv9vGvzDzWGV9vv7zOGUsrFau+V1Pp/aGP43+va/H3PQ8+XjGdrnJx1GCuZaIEwpVmbm0IO589nyOXri+b3mhBEYwGkFBRyNSYniwwPt6nKDk1NTXPZ2JcQsHVCYZPpqSItLSHyuebdToLfTEOanipxx50bcRyPYGhpGc/zJJqm7IF8XxIIGGrZvkmaAgGDSFRVfRzHW7ENlmIOJycKdPfEiScCTE2W+Nu/PsIPf3QL8aTKsXccSShk0HBcIhGL/oEEUkpeffksGzam+PjDcW7Z08sbb1xcJKbBoEFXT4ypqRKlmrcYvbogWKpWGnR1xvB9VtgTScBrjk84bFEo1Kg7PtHo1UWCkYhFw/HxfR/TNC+zPBJEgyaukOzqUL2jsw7oGjzYC+HmPNR1jVgsgKYL0jMlfvfbpol72OLM6VlKRYf7H9iMbeurctt9X5LNVEilgiSDYaSUzfm7eg6bmsQwdCbHMqRnSvhSUijUuTSWJx5TTgvpdIm+gQQzU0UOPrKNX/38KK+8dJb7HtjM9FSRc+fmSSQDxBNBTp+cYcPGFO+9M4rvy0VyHAgYRCL2YvW0VvM4emSC7/1gF3/zs084dXKWTKbK17+5nY7OCLOzJY59Nk2qJcSD+7fQaHi8/PoIVaHhxSK8NaNT9yStAUFbUOWzL6j7dF2nUfV4/ukzNBx/hTCt4fg8//Rpvv39nWRck46IWGZTJBaXtRcwXpKETGU2L1gaw+FWydMXJVtSgqAGgaDBgUPbePapkzhOg3g8QD5fw7YN9h0Y5t13RjFNjQMHtxKKWLw8A3d1Kt/f6YpGLGBhrGGIL4QgHrS47TLRftmVjBZgomoggJ0dcLep3AxGCpKpiopQRdcZSkkaQvJ6WjkA3N0l1zzX5VjrN0vXBNlMFU0ITNPkxLFpFX/7zGl8qR5GZ9NlXnzuDA9/fTsnTqSZm1Xxpnmp49Z84vba8xDU72HZN4kGBIYhKDnqHJJhdZxfNh5xrbjmVz755JPMz89z//33A0v+gHv37uXf/Jt/Qzqd5qmnnuJv//Zvr8qq/7HhakRkHdcX1ufCPz74UlKsuZydqTBR8Dg8IzgwFOSuDUEaVZ9nf3eOgQ1JBCodSgC+kJRKdUIhk4brE48H+OzoJIce3c4vf36Uet1FNj1OQyGTB/dv4Z03R9i5q4vTp9MMD7evKJ64rkfD8fjg/VG++4NdvPbK+cUo0XpTWBVPBKjVXB742hDvvzsKKGLy0KGtBIIGRz+d5KFDW3nnrZHF/S7EHL75+gVA3eTsZoXVl5LJiTy6rvGf/+MHdHZF2ba9nR03duA2JJPjeRCCm27uxfM8btnTy8njM1way63Yt+/55DJV9MvntVDL+KGwia4LpL9SbSOlMp0PN8m067i0N5N/LieFC+fa0RFlPlMmErHWtDwK6vDIRrFKzf9BWpKv+9zdDXVXIxqzFSnO11Z4fYKy54lELfSmV+jlxxyN2RSKDm2dYRprFxgB0ITE9n3GRrKrKpH5fI1CQaUR5bI1dF3j/cOj/Pif3cqZU2lOn0yzeUsrkYjFxHge2zbYe8cG3nz9ApOTaolV0wXBoMnXHhrm009V+8vCA8zHH05y590b+dFPdjObLpOZr1AoOlgBE0PXSLWE0AzB9FSRzz6bxnd9vvf1bSRSBrIgqXrKC3S+piI593Yoy7NEQFsSKK2BhcCCG1JxDk8r2ybtCr93H8xIDm5Yfa3ChuDObnh5XLKvXxn4t3aEePix7YyN5nAcl1gsQDweYORihp27ughHLJItQYou3NOl0eRc1FxJ3oEvknYVNgTbW9betiEGJ7KSMznJloSGQLCjRTAUl/z1GUlvWHyuNdZa8CVNays1GrlMmRMfnubAoWGqVZe64xKPB0kkAoyNZOnvjXPDjg7a2sMQ1nl9wuf2zst9ZZeQq0siprK48qWkXHfREEzl6mxsDWLq12cozDWT0//lf/lf+Nf/+l8v/nt6eprvfe97PPHEE5RKJf7X//V//YMjpgCNRoO5uTlaW1uvWkVax5cf63PhHxeKNZeTUyUqjs9ECUZLgu/tCHFDVwghNOVXmq2y48ZOAoEl0iSlWkZ2XZ+AbRAKmRw/lmbb9nb2HxqmXFTLti0tyhLpyEcTFPI1QiGTqYkiN9/cs4KEGbpGud5g5EIOz5M8+o0dzM+V+eGPbqZQqFEqOoQjJj29CXK5Cjtu7OKOu2za2iP4vqReb7D/oWElHBEb+P+z958xcpzpni/4C5cRkT4ryxsWWUWy6ClLeUM5Oklt1K02p0/f6Z3Ze2buuQtcYD8tsBfYL7vAXWAXd3Ex2AVmdmfOHNN92kst7y2NDEVS9K68zcpKn5EZGRHvfnirklVkkZL6OPURH4CQyMqsjMyMiPf/Ps/fZOerpNMy5vCDdy9z9kxmscMaIghkBy+ZstA0lbaOKLZtMDVZZGqyyJuvX+TW27u59/615PMO+UUfzbXrUoRCGl3dcRmD2Rah7jaYnS1jmRq6yjLALQiEgucJPjo8xr4Dm3j91fOUSkoTqFmW7Hx9dHAUy9TxfYGuKc0o1eUAVYLwIXRDgplGOMLfnvPZ2SrBwVKtBoQWapK7N16G18ZgQ1IQdxrse3ITf/jdKTKZyoox8f4DmzBCKkc/naRxlUeWZeu0tkWpVly8hrheowqA/pjK9HSRRGIlVQkhu8ZCSDBXcxp0dSd4+cVzfPrRGHfevYaJsQJnTs8Rj5m0d8Q4cWKKBx4Mc+ttPfT2JshmHVpbw/T1J3nvnUuMjxaIJyyqTgPTlBsWISSV4vhnU0SiIS5emGdqssimzW3sPbCJyckCmdkyd+7qpW9NsunFe7UtUswQvDsF8ZDg2Q3XmtdfXfmcw87BFB/PBlwqwIbktY8ZLwvcQBrV98ckkC+7cE+XBEm9EYV5Bw5OwcO9LPr35jh3NkNHZ5TW1iiZ+cpih00jCASXLi2QnS9z1939xBZt2yoeVD04vSDY0rJ69/R6YRmrl0LdE1QaK6NAQ5rCHe0yhjRuQstX8DYF+dodnTGMkIrjuBiiQWZkjA8rClY0QldXlC1b2snMVUmkbKIRg5bWSPM7e7xPWRFpfHUdnBHsWIxuHcs6TagugA8v5ulOmmxoD3/jGidfGpwmEokV/L4l1VVnZyf/8T/+RxzH4ZlnnlnxnO7ubl599dV/pEP9p6kgCHAcZ9W0lpv1zaqb58LXpwpOg8/GZBcqW1eZrAq6WmNs75ZgYnmKzKllY7Z63Wta8IRtnbsfW8+hQ2MEAt5+e5h77lnD8WNT6IbKzHSBXE6KlZa6mr4f8NGRKwIHt+GjGxoNt4ZnBdx+Rx/P/+4knidYN9DC5q1thCMhpiYL6HqRnt4E8bhFJlOhUqmTSoWxbY0P3hsml3O47/61zGcqfPrJOJMTRXxfUgH27h/CtHTefvOSPPZIiJaWMEcOjvDs4gh4yeD7s0+nOHsmw5//d7eRTFl89skU6dYIhqGSSodBCGZmSpw4PsWdu9bQkg5Lz8jF5BlfKPiLpvz5fI1Gw+OBhwYQQjA+licet7Btg8+PTZHL15rj2fR5m/sfHGDfk5soFa9knUdjIVpbI1SrDZJJi64Og54Wn8q1gl75mnXBeBmGSwIEbEwq/HSTgqYo+CLAC0X49KNxnv3xLUxPFVnIVonGTNraIjQ8H9s2mJ1eKQJZisZ0qi6WrZNbqJFut/GvA2rWxhXePlGhI2nKjcjiZ+stCyvQdJVwJAQE7DuwiRefP4Pb8Nl1Vz922CCTqeD5Pg8+NMDZM7O0tkVpaY1g2QampaMo8ODDg2zdVmVqqkgibmJaBpalUyq5nDs3x1Pf3sLkRIHsfJV77uunqyvOoQ9GmJkpEYubrB1oQTWU6+bIDyRUjmcDbmlVlnX4rl9L5vUbk7I7GguxQqCWcQTH5wXfHZRBJBfyMknpbB56onLM3xVW2N4Cv70sOJqBNhviKZtSuU75Yp2LF7J4jYBw2KBccSkU6jyxZwPlkssrL57lez/cgWFrdEUUZiqCcmMlQFWR/qmzi84SNw7LWFkJE3qjyjUboW1plY0padf0x5RpSwurl58/iTufQTWjuL5KfrrEjp1d/P3fHade92hJR3jsiQ1Y9pUUrNANXrTqCWar0CY11CTDBnNFTcYEC0HDC5jK17ENlb4v+G7/tdUfnRDV29vbTIf6y7/8S/7yL//yH+2gbtbNulnf7KovdsUaQsUPhfkf77MJLWs/LF+IcwsOp0/Nsu/AlbjERNJmw4Y0z/3uFAsZqRDNZiocOjTGvff149S8RasdhdbWCKapU1ocHc/NVjh8cJS9B4YwTZ2pyQLmLd3YYQPP99l1dz+5BYdCocb0ZImBwRZCRgt+IBXljYZPyNDQDZVGw+eTI2NcvpzDDhtYtkFff4rOrhi5nEM0atLSYlMq1Xn7zUtk5sp0dsXYd2AThw+OMjFeoKUlzL/793c1QUxLOkx7e5RDH46wZVsHvX1Jnv/9adJpG98XsnPoB+zdv4nz5zKsG0zxyN4hXnrhLKofNBOO2tI2jzy2nvfeGWZutkL/QJJ7711LLlclk6lwx64+HMfj8MFR5mbLzM/LLuaDuwexbYNIxCAckcBUEKB6Cm7dxwsWeb/LqGdLsa2XC4L3JgKGUgp71ygrvlMQvDMpsDWd9UPtvP/uZe65dy2RqEmpKH1kOzpj1GouDz2ynkrZbQLkVItNLBZC01QqFZfpXJFEsgvFWB0YBALWdtl89vEEe/YN8dIfzqwYh0ul9WbOn8sQiYTo7onz5Lc3Uym5XDg/TzweoqcnwemT07z75iVqdZ89+zaiaXDis6mmP2z/uhZc16OjM0YkbFAuu7z95kUe2j1Ae3uM3/zycx55fD0L2Sonjk3z2199TmIxgeuxezZiWRquf2NIJoTsQC51+L6MeX3UULi1TVIqdOVKtvxwCXa3CWZH801guL4zxpqYxsezATUfHuyWI+rvDsLFAsxUAlrjUVw06q5PSPgUC/I6FEJuEmMxsxk/OjtTom8gRc0TlBqwqUWmUZ1eEGxrUcjOOc2I0OXHvvfA8rCM1eueTgXrOqgmdKP25RdUAKRaQzz7zHomzwmKag9eAKZpcOhDeX2gQCFf4+/+usS/+/d3YUa+GF4dmYH7uxTMRQCbDBvsGkjhBoLzeSgt5PD9oEmTqTV8DE39RsSaKuKfIhj5j6z/9X/9X/nZz372z/qa9Xr9ZirQzQJungtfh8pVG8wVXWaKdYRQiIdD7OhLrPrYhuPz61+cWLGIRaIhbNvA83wOPL2Zy5fzvPLiWWo1T3ZNFDBCOt/69hai0RBvv3WJStll7boUO2/pJpMps7DgkErZtLZFOH1yhsuXFqhWGxTyNdo7ouzdP8T77w1Tcxr4viCRsNiyvYNIOITX8Em2hIlGDPwgYHKiRD7vkEzaGIZKJGLgugGvvHiOrds7uO2OXhynwdRkgXLJpbU9Qiplc+LYFOs3tPHGq+fxvIB4wuKOXX1oukK5WMepeYQMjbNn5hBC8MS+IS5fyjKfqdKStrEsgxPHp3h8z0ZaO8L4AsYXPOoLZQrzZalabrFRFIXf/Opz6jUPTVOIRGRi0dPf2cqxo1N0dcevWORoV0Rff/5vbufipSyVYp35bJVivsZ9D65j7UBqxaKsAdWKx+R4gex8hVQ6Ql9fYtUYy6UaKQrqgUKf7jEzUaC+aJcVi5moKgQBVCsNFnJVGm5ALB6SYi0BH7w3zF339DM+lqejK0rXmsSqI2EVqBSlb2sQCO68SwLx3IJDImHJ88jSePmlc9y5aw0nP5/m4UcHUYSCUCGVtKWbQraKHdbp7IwznylTqbgMbGilmK9RKDjEEzaeF3D54jwN12fdYJrXX71APB5i92MbcKouvi9IpWxKi9zaVNqmrTUio219sKP6DQHZmxMBD3YrGKqCCl8J3JVdwevjgod6FD5fgAHh8MFSitoqz71UlCP/oeQVcDRcFKQsBZF3ePWls+RyNSrlOoLFjvaBTZw+NducdNx+Zw/dW7v5LOOzKaUwV4Vb2+CjWVhvB7z6mxPXBddLXdd/idLcIoXf/J9wVJvG3f9nnvvN6RXixqVSFPjeD3awfsuSw4oUYq1Wh2cldcLU4PCMIBlSqHgC14cNCcHMXA6VAAU5LfKF7MRu6Y6SDP9pUs/+5m/+5ks1M2+C05uA5GYt1s1z4V+2ynWPT0akN6pAJWzqbO6OXTe+74sW4tZ2m+xCjcys9KUs5KWRdyJhEYmZvPf+MJX8Vd6x7WF23bOWQ+8PMzaaxzJ1covCmyVg9vR3toAiR+P5fI1EwqS3L0mxWKNYqBGJmkSjIcrlGuGwRSxmMDKcY2Q4T29fnP61KXxfNEfWgxvStLfHKJfrjI3mAIWOzhjxuEGjIYVRpZJLImnRmg6DAtl5h1yuSjxhkUzazM+VaG2PNYGuoWtEYyaxRIiOnjhCCAIheH9aJZmd5/yJSUqFGqqqsOvuNVQrDapVl7b2CKqqcuHcHIMb2vj04wluu6OHaqVBsVgj3RpmTX+K996+xNkz0p94SfjV3hEhHAlhRySY0oCZqQq/+vlxnNqVGb9tGXz/Rzvp7I5cF6CC5BwWsg66qjI8vMD4aJ7Z2TJDQ63cvquXctmlXK7TcAW+H/DxkTF2P7qeY59NsmFjG7Vag513dF/lGiCr4fjkslWCgKanbWeXtD5ruD733L+WI4dGmZ0p89S3t/Day+eJxkL4fsDghla2bG3n/beHqbsemqYyPS076I/v3UiqxcJHqrznpksszDu8+dp5jJCOrqvcc99aqhUX2zZo75Tm/6WS/HsqZeM4LqZlSIAnoKMriKpx5wABAABJREFU9pUA2fKx+My8QyRuE2+P4hk6/dFrgdKZnOBcTvBgq/hCYDhcV3E82Nl6LdjSgFKhweREXroeJCzssMGnH0/guj66LjmrDzy2nulIgrvboerD5/Nwb5eCosDZc3nefuMC9pKzxlVxuI8+sZ6+gdQfFeN6oyq6gngIbiTOMmZPMPKf/oL0o/+B442HeeetS9d97O5HB9n10FqOTPtcyAvWxGQ869W/f6wsODEvaATwcI9MhbI0COsKQgg+GytQqV3hdwtAmuAJIiEVy9AIhzTskIqpq8Rt/WsvoPqy4PSPHuv/ayld10mn01/J4uBm/eusm+fCP281/IBcpUGu6lGue5RqclEMUBnqitERv/EGIQDS7Tbf++EOZmdK5HMOydQVfpoTCCqmRe8ag8JChUTCxDMM2jsjVApVJuYcYgboypWF8MJ4hYJzmUce6GdqooDnB8TiciQZCmk8sW+Id966DAge2j1IoeAwNpLj048naDSCxZF7mK3buwCF8dEcdddjYDCNZRvkFqpcvJAlHreoVl00XaHq+Py//+Mh2tvCbN7WiYLg8IcjtLZFyWbL3H3v2uaSpqoKRkgmrXR0xIjGQhw6OIpb9zl+XIZO2LaB4zSolF1uv7OHrt44vpCj9g2JgExBo1KqU6/7FIs1/vDcaeIxk66eOOnWMO+9fZFiqU4mU+WueyRwrbse3T0JOrtihEIqO2/toacvSTQaor0jhm3pZDJlPj4yzu7H1mPYGtWKdw0wBRkV+qtfHOff/sWuG44+VQVGh3Os6U/x1huXqFbkWHhmukSt5nH3ff1ytJ+vEomG2LN/E0cOjrJuMM2xo5PsumfNqsmViqKQzVSo1T3KxXrTSxUFdE3BCOkcPjjK/CId5MihMfYdGOLQwVGEgKMfTzI+muee+/vRNZWZmRJ339uPqqu8+PplHnign9aOMFEFJsaKXDif4dEnNmIYGrkFB6fqMrA+jed56JpKNGajahqO06BcrpNqCXP69Azt7XGymTJ6SKOrL/6lAVkAGLZG30CKWG8KFUGmKshWBW+PC1osmTe/NSUB9IYEjJYUSvPFL1T6NxJJUtbqx1FzfJ777Unue2Ad8/MVLl6Yl44LyNdJtYQxTJ1wOsbdcflveiCaGxRVUXBLDr6AnCua57wvBCrStzi74NA/mGIpfdYPJH+57MG6uFTz/zH1WUagqdAdAV0BZ/Gg1sUlHSDkVZh7+z/jomEMPEA6G2blybXydVvSYT6Zg4gh+PGQQrVx7WMAusLwiS+BaeoqoZaiKNy6JoHTCNBUhUItYNpRmJgvYwkXXEHF9chWVqZadcRDDLSFMfWvN0j9ovrGr8Kapl1j5H6zvpl181z46hUIgeP6hHQVPxCMZmvMleokbIP2WIjOhASYQgichgSjxZpHpuQuPh+k7bsCqIQMjfXtEdLR63nlCV4dE7RYCoMJqbxdWoj7lVRT2RsgraUmywEflhRub4+zsV+h0hD8+mLAt3vDtEY05ss+EUMQUqXSPGWCV3NpSYfZ/dgGNFVhaqpId2+CdDrMxQsZtu/oItVioaoKZ07OYYSkeCSfc1jIVtm8ZQuFvEMhL0d+uq5RKtXxfUF2XvI5K2WXkKmxZ98QBw+O4XsBF85nOXf2StLXt767lRPHi/z2V5/z0COD+HWPz0/MEIubpFJhOrujTIwXmJpYmcRVKbtyHVQg2RJeYetU9xXW9kQ5vpiAtLSmFYt1KpV5tm3vJBwJUSjUVviMRmMm507Pce+Dawl8wbtvXWLTlg4q5ToffzTOli0dTQ/X2ZkS/etbmBwvXANMl8pxGkyOF5aNPlc7tyCVDvPxR2NNX9AlUdiRw+NMTRf57ve2Yxga+ZzD8KUF+te1cPjgKKVijXQ6QqPuo5naCmCnqQrlsku95qFqavM99q5Jcvc9a/i7v5avoy7y+oqFGqal8+DDAwghY0pjcYtUyqJScdmytYML5zOcPDVH1YNXXjzL49/ZSRX53sdG88zOlBna3MYjjw5Qq/mMjuRIJMMU61WEgETSpK09jNfwef3V8+y6ew0fHxknGjVJJO3mSPerlBCCxOLkN5ZQGADubFdwA+kV+uKoQFUEW1sUbF2hmPlySv/VKLCKojRtrI59Nsmtt/Xw0h/O4C8edOALRCB4Ys8GjEadoG5i2BpecMVUYYlHHtWVazqmDSFH3XrU5hfnA8K6wAvkNT6YVEiEpFXZH1sPdCuMlQRlT3aeDRXcAF4fE7TZcL8yTO7SMbR4O7Mlm86uGJZlUFvl/LbDBt09Cep+wLkcLNSg7svP+t5O+VkvVUhTSdqCtH0lvODqz3VpchQ2FZIC1PYYJ6cdam5Ab0yjx/bl/dX1cVyf2aJLttygN2XSn7b/ZFX+33hw6vs+1WqVcDh8Q8Pxm/Wvv26eC19cXiDIVxs0fMFsoU6p7uEHssslpPgagcpCxWOh0iBTclEUyFc9vCZKUpC9EDANjXQ0RMLWsUIaUfOLbkkK29PgePDRrPQHvKtDLiZXL95hXWFXh8KdHYK3JwIOz8C+NQpPrVX4JKtw92ObeO+1s5QcH08ThDVFUgKe3ESlUkdRFU6emuXW27p545ULxBImu+5eQ6VSZ3KiyMD6NH/+b25nbCzHwoJDT08cgeQ9+r7grnvWoGoKlm1gRUIk4yZmSCXdGiaesInHTXLZCrvu7GV+tkS5dIW/Zlk6lqXz4EMDvPD8GV598awU/SQsZqZLPPDwAKqurCqAEULQcH1paB8zcase5uLKHXWqHDkxyyOPr+eVl881u5EAhqGRStk88NAAb71xgex8tWlt5fkBT+wdwrZ13nz9IqVSnXNn5ygV65impBDoukql7JLPOQyqCtn5G0cVZrNVhhY5pKuVEIL2ziivvHgOXdekKGy8QDZbbdplHTk8xpr+FJcuZRkdzlEs1ps2WJm5MratI4B0WwQ7rDfPkVBIp+56tLZJMVyxWOf0yVkCP+DPf3Y7w5cWKJXqxGIyDvbD90e47Y5ePj8xjVNtYFk6992/lldfPs8dd/Zx9JMpDEMlpqkofoC7UOLljya467YubEvH8wQnPpvG93wee3wDA4OtZObLVKoura0REkmbSrlOdr7KLbf1ELYNTn0+yxP7NhKOGKv6xv6xFVIVNiSh1ICpiuD9KUFbGAa/pNJ/tVrunrGQrcrEpL0bqVQaTdFaV0+czz6dYHB9K6+8cI69BzbhROxmJ/ZGgi5DUYiENTauibHVljuvfF3wwoigM7zSceCPKVVRrrHpAtjaojCeLTP+yn8iCCDWvpaxWQfVtHn2Rzv55c+PLwJUSZewbZ1nf7gTTYOtSYVWW0YYCOTI3lxcVpZTL0IzDiM1m+6uGzsSRA2FqAHyHhhBIDg+D+eKAl2F+7sVtMDj0lyFcs1jJFtDURT603+aKv9vPDj1PI9MJkNPT89NQPINr3+Mc6Fc9+RNzNL+ZHesy6tS9yg4HnUvoNYIyFcb1L2VIFPansh/CekqPSmby5mK5IRWFr1HUdBUlbv7QowUVcKmhmVoJGxpubM08nIDwdkF2LEKp22puiPyZ4MJGCnCa2Ni0eT6yu85Pi+B62BCdkQf7VMpNeDITMBgHB7uVqgHYR58egdGucxUxqEtbdPbHcOK6Jz4ZJKLFzLs3NlLveax76nNlEs15jMVojGT2+/sRbUMpsYL1F2f9RtkJKLjeGzd1kmhUKNe89i8tR0jFkJX5OLbP5AiNm/x+9+cIpMpA7BpczvPfH8Hly5lyc5Xm1y9t9+UnDbpIapSrTZoucpW52RF485Hhzj4+lmEF+B5AcVCDcPU2LtvPe+8LQVf+w5sIhoL8cZL5ygFKmu7onz3e9sZvrSwyJu1CEcMPnhvmJ23drP70fX4fsDCQpVo1KS9I0okHOL556T3aDxhUSm7mJbOnn1D2LbB++9exvMCUikbLxCkW28cVZhOh1ftwi1VoS44X9I48PRmfvOLE4yN5Hho9yA9vQnm58tomsK6dS3EEiZdXXFM02h+docPjrJ1WwexuMmlC1kWFqo8tmcjuqGSTodJpiw8z8dxXPYdGOLlxUCFpcSmh3YP0N0TJ5dzaDR8brmtp+laYFk6e/YPcejQKPWaJzvGhRqKAm1tEUQAuUwZK6Rx7NgUjz6xkXffuUQ95zF8Oc//4//+Prff2cMTe4eod3pMTRUZuZzDNDWOfzbFrrvX8NYbF7EsnXjCoqU1vOpIXyDIODBVhvVJCV6+St3WpnBbG0yUBEfmBB3dV4Dh1Z3L5Ur/1arpnqFIzm52vsofnjtNImlhWQbnz7rcedcaLl9aoLU1im6ovPLiWZ54Zgcx48r42QpLS7BXXjxL3fWbnf0lHvly8JY0FXakYbgIneFrj+kfq1qdYTKjnwECDZ9YzOS9ty7zwMMDK4IUljZMqgbzRZfemEGHfe13cjVXvtQQnNUVIpbG3v1f7EgAcmJV9eCWVoVbWhVKruDFEUF/TOOWnjinxgs4rvRNXdNi/UmuRd94cHqzbtY/Vk0X6pybkd2ipK2zsy/2J3lTWKpj40Xy1SUy/hIvU2azW4ZGzNLpTllEQhq1RkDF9UjYBoam0hINMZ2voamS2B8xdXrCDRqf/opNtz+Lq18xP5+uCI5mAnb3KnyehdSX1qLJxJe0DUfnoODK0VlHWPLozuZhoQ5bIz4Lc9IzcUOLjVuLUg3rhHWwIhqnawm2b0/y8Zzg9KwAJWBt2GZ0usr0/CViMZNk1EAoCr4nqJQkELFjJtHOOKm0zQcHx9m4LkWtUsf3A/rXpejpiROJGfiIJnivlBv8zX89umLcPTlZYHwiTyplMz6W58L5DIVCDYTsoCqKQltblFSLjaJJDt7S4nVrK1S9MPc+tQOjVGZseIF4XALNo59OUsg5aLoqR8V3rcGpeTgeqJrKqy+dY9c9azh/LsP5c3OUS64cDzoNDjy1hfGJPG1tUuSkKgqXL83z6GMbKFdc5ufKRKIhOrvi5HNVDn04wsJCFU1TaW2P4vsBPX0JbMtYdbRv2wY9fasr6ZcqYSrc2QaBG+HA05sp5B0uXsjS3h4h3Rrh9OcznD83z+N7N3L44KiMOD3rUizWURRoSa+j0fCZni4iBPz+Nyd5fM9G3njlPM/8YDvRaAjX9bHCBnv2DzWtqZJJS56z0RBtHRHm56qUy3XWrkuxY2cXlm1w6uQsszPlpgcsyA2a2wgwDJVo3KRxKcvkWJ5ivsZ3n9nO6GiO6qIdVqMR8Mar57njrj40VWU+XyEcCXH/QwN88O5lRoZzHHh6M+m0jW3reKt8TufzcHBasCGpEP6jV3KFjgiAaHp5/v75MxSqPklTAtTVgOHVFYiA9sWuZ7XSaH6vxUKdfK62Ita3UKhhGBrVaoPcXIlYTwIVCDxBqdKgXKrzyGMbKJXqVCou6bQt08lWef3NLfD+1Opq+HJDfGXAfnVli2Xcd/4zER3yPniZYdpbQ1QqLq++LOkXmqo0/YXn5srEYiZWOsonswG3t1/L+6w7fhOYCiHQA5+GIyg3PF5+4Qzf/9HOLxTABUKmg+1dA7auEAspbGuVYQluw8NxfRQCVPVPN/XwJji9WTfrH6HGFxwuZRxAIUAh73gcHy+xviP8JUbVX59aEikVax75qodAJWrpRC0dy9AwdQk89at89uyQxsE5hdaawra0YL6u0pcOrzCgVuYukHvvv9Cx7k5o39b8966IwlYfXhyRIM5uga8SaRgzFB7qkc/xheBsDj6cFuzuValkq/y3vzkLvo+1NFIzNO57fBMb+sL0RhTSpuCj2YChJAwl5WJSr8Y4GtGoe1CruIyVXEwEpWINPaQRKCq/f/4cuqHxw+9tYd+j65idKaEKnd6++KL4Q12hRldVdVUeZqlYRxHw/rvDPPDQOrZs7SCbrRCLmrS2Rag4csF+6Q9nVrUDCuvQk9aZLKvMLzicOZOhVKw1044URSGZCjM3WyafrxGPm5w+NcMdd/Vx5NAYt97WTbXSoFSqE4+bbNjYxgfvX2bkco6+/iSbt3TwzpuXCJka5coIibhJe2eUnt44H7w3zNnTc8TiJrqu8dgTGygWHNqjccIRne//aOdKUZSQSU7fe3YH01MFNFWjs/vG40w9pJJqsfnoyBhu3ef82TnKZZfO7hgHnt7M++8Or4g4VRRoa49gGBqBL1jIOhiGNNQv5KVf6uuvXuCh3QOETA0jpOPGffI5h1jcJBYLkUqFUVT44J1hunsS6IbC7Xf2kltwyMyVueXWbrZs7Wh2U5fK8wLCEYOerhgfvOXQaATMzZZ57nenePDhATJzZS5fmmfdQCvRmMXHR8a57Y4ebr29m9mZEsOXF9i8tYMHdw9SrzWoVhtkZqvLvnPBZEXaN81W4aFuhYHEPwx8aIrcEBYbgnLYZvd3dqAUpKVaMiWnCdf7fhZqgokKdIfhyJzKo/s28fLvT1Eur6SoNEH8Yhb91GQBgGLeIdydolpyyedq/HLZuRKPm7S2Rnhsz8YVpvbLy1AVHuld/f1/MgsbUjLa9Y+peUcQMwT63d8lCL6L7gVEVR8lZrD7sQ289cZFXnjuNLGYSTgSwvcC7ntoHfGEyTwq01XBsYzglrZl98Bl3FzPCygUani+FGMBlEsukxNFBje2XDdAAkBXIRmC6SoMLEaytloyNGFni4aqKAih0PAFhy/n6YyHaImEiNt/OmvRn86R3qyb9TUtLxAMzzsIVAxdxdBUqnWPvOPz6UiRDYsA9Z/yxlD3Ai7OVag1AixdpeELal6A5wt0VaEjESIZNrAN2fVcKiEEFdcnX/WYLdap1P0mr0ygYoc0blmTaO6+j2YEegharGuPYXePwskFeGlUEFLhaEaOn+7sUBi0qhTf/U8UXbDf/U/Ev/N/w9WvjH3XxmUX9GRWcCILW1PiukbTV2INBfOOWDHO1xSFrS2QCMFszuODF89i4eMIKUzQVbB8nzdePsuaP5cdCltXeKhn5WsZFux/chNvvHqRhhtgKILsQg0jpLFv/xCHjk4TCDDxefUPZ/jeD3fQN5BaEbd49WKuqVyXh/nxR+PsunsNr796nmTKxtBVpgQc+Wic++9fy4lTU7iuvyJhZ3lFDYXKQoXhi1mCxS/Q96XgSSCYmy2xdVsHKhIQqAocrU5y566+pmn/5q0dOE6Dt964wJZtnZw7M8/Z03Nk5ircc3+/XFAbAbqhEouZ1F2fTZvbiUZDrB1owa37HD44ys7buulcdAjo7I7wb//9LibHC8zPV0inI6Rawnzw/mUunJtHVRRa2yI8+a0t1x1nSleGMPsObOLSxSwLWelFun5jGqfaQNdVYnET3wvQdBXb0nnw4UGi0RBvvXFR/o5AIISgXK5jmjrnz86za1cfLa1hCvka0ahJJBLCdT1c12cuU+at1y+SmSvT1r7AQ7sHOX8uQ3d3nFMnZ7HDBtt3dFG4yoosEjHYs3eIkKGhLNuYzc2Wee+dy3z7mW0yKCLv0N0bI5FoZ2oiT3a+Sv/aFL1rknxyZII3X5NduWOfTVEpuzzzwx0cK6qcWhB0RkAEksbyDwWmIMF80YVXxgRVT2BrKiEtwcBAimIg0O1rz+WlsnXJGf40I2i34VPH4tk/u4Xzp+fIZqukUjYhU19BiQiHpZtEyNRJpWyiwqde85fxN2UVi3VKxTp/9zef8e++wNlh1WMzoFiHnhuzS65bB2cEj/VFEWsf5NeXBN8d9MgWcqSMCIpe4eFH1xN4kvoSi5m0tkdkOEUsxKezsHeNwtWuTkvcXCGEdDK4CoAGQjA+mqN/bfKaAInlca6fZwXZOjwUu/Jzx4NUSDYKbl2b5MJMmaLTwGnASLbGSLbGLX2xPxl/1G88OFUUBdM0/2Rb3zfrH6++6rkQCMH4Qo3xhZoEJMBdAykA5ssuZ6fLIHzOz1YBCIdUOuImPSnrms7jP6QafsBno0VqXoBApYTHFSMWhUYAo9kao1m5kHbEpQApW25Qqnm4iyoRKVKSANvUVdriJj3JlXyloRT86oLg3i5Yn1iZl64qkgO2Iw2jJQk0H+tTeGNckNYu4wx/hqJAbfQY8YXhFd3TpWPdllaYrAR8noVb2lb+9OpYw3jKxkhFeWNeozssWJ+4EhXYGVE4O1FmvuyTMqU4KkBQ96HmQ1zzm0k1q42WgwDsqOCZH+wgM1dhbqaEpqpYkRCHP5uhlHdImqCgNG12+gZSq3pqLpUfcF0eZiZT4aPDY3zvBzvI5xzGRvPEEhabIqEVBubLX2v5cQsUYtEQQggUBcTSCSl/2AwQMC2NYsGjVvPI52tMjOdJJW06uqLs2NlJ4AeMDOcol10OPL2ZYqFGedFGK5EwOfrpFFOTRep1j8f3bOTcmTnJv1Xhl393HE1TiUZDi+efoOgJLtc0evtaSKRs3nrjIiPDucXzZXGDkal84TjTFwI7EmLk0gLlisv5s3N8fmKa3Y8Oct8DawmEIJ+rEYuFaEmHsSydt16/2OxqCiEV46mUzZlTUvg0MVGgpz9B1PTIzRc4ea6EUFQ6O+IsZKusXZfigYfWoSgK7759mbnZMv/j/3QfhXyNmekSlbLbtKEqFKSH7uatHdTrDTQDDjy5uRlrCRKg/t1fH+U//B/uprsnTrkiP9ehze2AQq3e4PDBMRRF4cDTWzhyZIyJuSpuAGdHSphtSX62WV4nAI3rnGtvTwh291553BfVcBHWxCR/UVWkAl7en65wy69Xtq4sXqfyMaWG4N1pjbvXtXD2zBxTkwWmJiWtYqmDeuTQKLquYegq7ekw5VyFQr6G41xF/1DkETgVl8mJAus3X9/ZYbXyA8GnGdjc8lXvtYKjGUkPCuuSz9kIYKGuEDNNpqdKvHdokntu66Lh+c3OqVP1OPThKLfd0cPu691XFh0oGg2fQCzdd1dWPG4xO1NqBkisFuc61BkjqqsczQjuaFc4NCPI1kSzixwOaezoizd1Ahdmy6gEnJkus6krSupPAKB+48FpKBSip6fnX/owbtbXoL7KuTCZqzGVr1FxA5aEQYqmU3QlV64tZmJoKhdnywQC6g2figvD8w6Zkkt30kQIqDUCUhGdlsj1rJNWr4YfMJatMVusrwCXrTGTsKmhqQohTQqPSo5HplQnX22gALNFl9miy5KFk0DBNDS6kxYtEYPIVTSE5Tv2iA4/HoKDU4LpCtd0HBefQX8MPp0T+AK+21en+NYreG0bsUSA1ihQXKV7ulT3dik8P7xyHHYjw/0nDgxRM8O8NBqwKQWbUjIOMKg4KEC+DilTsNR4kVpfaf3Ur6RWtejRdZ1YXNqKrR1M4dY8jn46SaXs4gYC76rn3Oh3LVUQ3JiHWS672GGDmekSCwtVLo/kaNQaGMZKcd1qr6Ugk6rMkE697l1jS2NZOoEf8Piejbzw3Bn8pQx5AW7DZ/vObj46NMbmbR38+c/uYORylrnZEmvXpWhJR2g0fOYzFdb0J9m0uZ1IJEQ+7zC0uR0hBG+8JjuUIVMjEbfQkN22hg+RkEI5W2R+pszw5SvAtPm5CEGl4jI7W6ZvXfK6PFTTUnno0UGe++0pSiU5xn/7zUvcc18/tm2QTtskEjblsssLz51ZMW5XFGnxY9uS96iqCuFwiFK+TsIbx3/x/8qt3/l/MrOgsLDg0NERJZ2OcPjQKGMjuaYl0seHx/jRT27h539zbIXVVkva5tbbunGqdZyaTz5fo7U1soLLuiTWeuety2zc2E48YeK6HuVSnfn5Kp8dnUQEAU/s3cTsXJmRyTKGKmkbquOws32ljZNxnQ2uJwTHMso1m7vVquQKPp0TfGfgSh79P8QeM2Yo7F0D41MBT+wbYnwsz6bNHSgqWJbBkUOj1Os+tq1z/4PrqNc8ioUa86tNFJZRSbPz1zo7CATjpUVv0ms+C8nFna0KFmrSeu7L1pFZ+dne1SH5mq5QiBgBCUsnaaUYuzjF3LzD++9dJhY1V/gKw43vBUIIOjujqNcxyjctnWjMZD5Toac/gRA3ChoZYl4J86uLAaYGT69b+TsVRcEOadiLVlSX5irUvYDj4yVsQ6UzYX6tlfzfeHB6s65fSyPfi3NV4pZOf9r+RmT63qgqiylG8r4jQWkqEiJiauiWxR9GBD8ZupKTfMc62Ul1XJ/5sst4tkq5HjS7qaAwma9x7/rUV+qmnpoqL4qVlCadIBzS2NAZRVcVsjWB40G7qRA1dboWRR6jWYd8tYGmKkQXFfNtsRD6KjfLa3bs6TCdnVFsW+eB7oA3JgBFwdAUhJCdiuXAYjChIuo+w9NFMuFvE90VI5EwqZazxBqTVBamMdoHAWUFAI4Z0G4LLhdEc2y5XESwVAKB6/q89uI5vvfDHTw9oPLBlAQ7W9MKLS02SVOKoqo+hDVwfZqZ7zeyxllefiAImRqz+ToRHeq+tK5SUJqL59V+oterVXmYSIHQ93+4E5Dd0YsXsvhCAgVVUYgnLPRFVfOqx70IMp/YL3Pig6ty4p/YN8R8psLxY1M8sW+jTDWaKpJIWqRawtSdBufPz1Mo1bn99l7WDaYpF2vkcg66oZFIWLS1R+jtSzI7U6JYrBP4AYqqcuTwGDPTcmS7d/8Q+byDEdJJpk2SpkLaVrgw2aCYd9BU+R0HQp5fS5jb9wT5BYf+geSqi3ogBJdLkEpF+MGPdzI9VSI7L50TWlrCNBoeRsjEsjQKheCacbsdNth3YBOvvHQOkCCgrS3Cay+fZc8uBW/+Mt5r/xfWPvO/YJg6c9NF2jtizE6XVgD9E8enuef+fv77/+FuJhbH8enWMD09CaqOS6ngcv7cHKGQQVd3HFVVOP7ZFLqhNsVaiYTFtm1dXDifYWhTO/m8dIG45dZu+vqSnL8wRzRiodQbzTz4L3uuAuxqV/jtZcGW9I3z5F8fl5nte9Yof/QkJxBicaOxeJ36guNZBW+swKVLWR68qxddhZAlAyi27+jCtHTS6Qi6ofD6a+e55ZZu0unrzN4X33O6dXVnh2IDPhuV3NCOsASp1Ybk0dr6Yhf4S741LxAcnoW4IbvIdcdnelqmbG2K21h+DITkPyvKYgRu2W2C0qX6ou/KtHWefHozv/rFCep1r2nDZ9k6e/cNcerkDLfd2YuiKNRqHi+/eIaGu/LNS4qPvO8FgYL1BUiuK2mRihiMzleZK9apNmSjpD0WaoLXr1t948HpzcjKa6vuBSxUGozOO9Q82RnMV2U3ZrD9n9Cz41+4bnQuLFQanJ+pLH4eskvZEg3Rnw4TXbwzXC6I647a7JBGTbNY1xWiVHaYK9ZRkONKhGCh7NL+BYlIy6uwKFYSwMbOKJ0Ji4mK7OjpyG7hRBl6o1eeoygKa1u/3Pd3dadSCEGjEaBqCk89vYWWVpudSpVPDxaIxUza2qOYpoZl6+ghCaJ6/Cq/+cUZGo5LoeASBIJQSEZdfnrB5a54mC5Rp1IzVoysOjpj7O7VeHM8IEBlS6vC5EjhGu/DoguqIohxZdT9YHfAu5NwqRDQ1xnDDOnEAo/yIg7UFOmZ+EXWOJ7XIJvNLiaGGcTbYqBpNAIfL4CUqVzfT1RV0VQ5xr96DOmzkoeZzVZJp8P09CYwLY2f//Uxdt29hpCp4y6Og4NAUMhLnqURUmlrj6IIsWIcWKt6NNyAU59P88TejdTrXpObaYcNjn46wR139jJ8Ocf0VIkDT29mdCTXfP8PPDSApqnkFhzeeO0Ckagcj3uuRy7ncMeuPl556bwEhNEQiaTN1m0dBIGgvT3GwECacNjg7OkZtmzv5sXnTze5sYGAcMQgmZRdGnUxIGA5cFA1BS1qXXdRbwRQ9aBXBFwezbFmTZJUi0U+X6dcqZNKhYlEDRbmK3z4/siKcXtrW5j+tSnefetSk/e4/8AmGfU6UWRiyzr6nvnf8D/5L9Syk8xMhjn4wSh792/kz392O/PzFTJzFVpawrS2RXjlxXMsLDjs2beRlo2t1JwGszNl6nWPgx+MoOkqO3Z0cuTgKDtu6ea+B9dSLl3pnkZjIeKJEEORdvm8WoMNG9OYlsE7b11kw1Abp07ONN/7F52rV1fCVFgbE0yWZcLRahUgyNXg2wPKCtHiV6lCXfDulNwEh1RByoQpBx7tVWh02Jz6zOH3L1+gLRkiEQlJuglQzDls3taJaWpk56v4gaCzK4pl6c2QhaVSFLAjIXp6EyuupQt5wUIdMo7A1iBsLG6m/cUNjwKzDrTZ8JvLgu6I4P5u5QYJUoK3JgQbkwoDcYX5uSq/+t1ZyoupdVEDTtoaj+1ZT3tnFCukIfCvITx8me/KF4K29ggHnpK0mXyhRjRukYiHOH1ylnKpTjxmcurYDIahUXF8DE255rWWKD697UnO5r6Y7mAZGkNdMdriJpdmK1i6hml8PYEp3ASnN+uqWs5fBBklCfLCz1VXT3v5JtRo1mlyOgWwoy9O1DKaHYfPs4JLBcFPh65/o0+G4IMphb39ETZ0RAgEjMxXmc5VmcrXvzQ4LdW8JjDZ0ZdoEtxdX3p+hnUpSJIY8avzrT6ahVvjQROYLqlKg8XWxd//4jhP7NmIEIITx6aZmytjmrr00oyGFr0NdV578Sx+3adQcGk05HMdp8HLL5zh8b0been1CZ75Xprf/vY4jYaPunisoZDGvgObeLQ9xMjIPEeHPWpVCW7VZR2eiAGVhuS5zcw7eG0peqMa/Uko1CG3aKj/yotnURWfSkNgaqt7Jq5WgZBq5NMFgRto/OC7m/n1786gq/61fqJvST/RPfuGcGoNpiYKpFsj9PQlCEf0FUpjHzAjOq2DrUyl4HRNUHMhnsnjuj5HT0yzZ/8Qr798jnrNw/cDOeFUFO5/cIC3Xr/I3ff2NwVEiqIwN1Ni+FKW2+/o5be/Okk4atDaGuHixXlq1QZP7Bvi2NGpZhf1/XeHmZuVtjeGoXLq82mefHozb75xEbfuL8aC1tB1hXvuW8uxoxM8vmcDE+N56nWfRMKiXvf5/MQ0bt2j6khj+nvvXcvxTydpNFZyY9OtEZxqg5ChUat7KMjYWD+QkayhcIjOzuj1R/qawva0wvR4kZrj8b/9vw7S3RNn561drFmTYna6hOf5DG5o5clvbWZ2poRlaaxZ20k0ajAynCcas3j08Q0kUxaWqXPwgxGcmsfEZJVTecFdd//PdEVUolGfYqGG43g8958/4d/9xS5mpop8dGRsRRrXX//Xo7S1Rdjz5BCe5/P735xEURUSCZmo9eH7I/StSbB2XQtzs2XCEYNINER7exS33qBeD+hfm6RSbTAzWcQIaQyub+X4sSnKi924L3uuXl3dUYWjGUF/nOZ1deW8FhyeEVS8f9gIP2EqPNIL53IwVRX0ReHBHjkBaXTGiFo6husxveCyUHDRVclR9RoBiYTF+Hge3ws4cmiMvfs3LjO1vwJQI9EQP/jRzmuuoXVxSNalV+v16A2yFO7xBacX4DeXBH0RQcpUSJrSEL8rstTxhYc66pimxcSCx29/I31WWyx5H9AVyS1/5aWzfP/Ht7HvyU0cfv3cKqP2L/dd6SGVZMrm8+PTaLrK2FSJaqVBOKRw731reeetS03+djHnEEtYhIyrv8krqV3vTcF4WdAXXTmFuvp60hQFG5VkIyA/53CxLujtiREJq6zc7v7L101werOa5TR8jo+VqHmyY2UaGutaw9S9gJFMhXLd59RkiQ0dEUJ/4rm9X7U6EyYFx0NBkIyYRCyD310WPNANGQdGSoJHe5Ub0h4cD0xd3hQVRUFToCNhMpVzyDse82WX1uvGdl6pWuPKrS9qXtn5DsTlrj9fF3yeFVwogKYG7Or4Kt+VQq4umnYnQsiunecFzYzsatmlVKxz4tgUd9/Xzx9+f5p6zeOVF8+y98AmhBD4gSAzVyEcNprAtHn8tQbViosCXLyQxQyp5Ko+MUM0BUbPP3eKu+/u5w/PS6uW2+7oJZOp0NJiYyzu9nVFIR4SeEKhNWUTWihw/PMi0ahJX3uUcENHjRgceEqClVyhTkdHlO4vsC5aKiHgUlFheyygPF8iX/b4wTNbKORrjI0skIhbRGMmp07OkFuQZu1/89+O8vieDbz37jAAtmXw/R/tpLM7smJxFQh+czHgWwNyoTy5oDI661BuCIpzDubpGfYd2EQ+5zQ7bt09CS5ezDA3W16h2lcVyGYdLpyfp70z2uwaViout93eQyQSYvjyArfd0YvvBxz6UCqnFUV2LN1GwHymSiJl8b0f7GB2psTCggMCkkkLw9B4561LjA7nefDhAUIhjZCp4/sCTVMoFeuk2yKoCrz5+kWcaoNINNTMQRcC6jWPUEgC45dfPEu9Lq8lTVdpa4/w1NObScf1G34nqgKZ2TLhSAjT1JmaLDbBYjwuRSnHP5vmwLe2ABBLSKP9el0jFNLo7o5hh0N4ns/rr11ojmOjMYuzZzP8/d+f5i/+h7uIxaSi/8ihMfbsH+Lddy5z+529XDg3L7t/i2u4Zek8uHsA2zJ4+/VLi8BUxtoe/WSCZ398Cy+/cIbunjgPPzKIYaRwnDrzc2VUXSUet3DrHp99PMHawTRBIN0Gtu/oolb3SLdc39/zRvXxrIQZXgCHpwX3dl25Jz03HFBywdKgxZSP3dUJ1/blvlz5AXRG4LZ2ZcXvMG2NPQeGeOnFs0R8n6on40DDlsaBvRv58IMR1m9oRdNUeT6/dJ49+zfyb/9iF9NTknPd2hqhuydOImXiXjXT11WFNhvydcFMVRrSK4C3yIvvikA8BHOOBM8ZR3Bbm4LrC0IaCEVhuAyfZQVDCeg3K3hHf4V72w+Ym3XBl8BURVnBj3brPpmZEv3rUvT+cAezs2XyCw7JFpuOjiimreEjqDRopjmtVtKBwmbfU5uYnSkxOefQnrbBD/j8xDS5BYdINERvbwIQlAo1kikb/aou9xKFoCsMXqDQqPnMzZRZyFabU6ildDjPDZidq/DC82fwGgGaphAIQdg2ePLpzbS1m18rgHoTnN6sZhUdrwlMt/fGSS2KdAIhqDd8Dk84ZGsNKm6RgbbwlwJS/1rKWpZiYuoqhqrwaC+8PSmVnN8fvDEwFQjO5WW28nKFu7UM5Ge/JDhd8TKrOAskTYUHuhXu6xL81VnBlpavZkY9kFCZOufIfHrXp7HYRV+u3y0Uami6SrXSIB43KRbr1GpS3GHZOnOzZQxDaz53ZSkUCnUsy2BhwSFs6xjFBhUPorocY8/PVmR8ZNykVKwTiRiEQhq5BUcq3lXZBVBQMHWFoOHz818cl3nxQE9vgkcfX8+rL5+j0fCJJyw0y2RyPE989yCm/cVCANvUGfJcXv3N+WaHJBINsWVbBwsLDnOz5SbACQJBbsEhCASVSoNoNES55OLUGvzqF8f5t1dZ4ZzLQUcYWkz5vexMw3CbzSdCbl5yuRpvvXEBAUTCIS5enEc3VGZnpMhnhWofhWg0RKEoeZavv3Ie09KxbYNMpsTtd/Zx6WIWTc9xy63dFPIyRGAp5SndGubRJzbw1uuXeGzPBtYMtmBZRd556zKVskt3T4zvfG8bU5NFZmfLtLdHscMGTsWV1jmREB9+MMLwpQUAmRDlBSQWF07X8fnD708TiYa4/Y5efvLT28hkKpRKNdLpCG09Cayo3uwCX6/rs6R0PnJwlL37h3hlMdUJpO2Q6/p87wc7yMyV+ejwOJu3dPD2mxd55gc7OHN6Dtf1yMxVmnzUWNwkpBrYYYN8Tv7bwoKD7wfs2T/Eqy+d4/DBUe66Zw1O1eXZH99CJlNhPlMhFjPp6IxhGCqz00X2P7VJGsznHVRVwTJ1QiGF7/1gBzMzJU6dnKWzM0Zbe5RYwqJe9wiFNM6cmqWzO87BD4a5c9caXnv5HA8/OsjQxjb0kIpmql8JmALc0QHTFdjZqvDmhOCTOcG6uORgFlwJKH8yJO87H8/KZKE9a64vsLq6AiGYKMOamELCVEisMvA5MidwPJuHnt5Bcb6MX5aeqclUBOH7TE0U2bGzG8vWKZfrzM2W+ev/cpR43GRgsIVIJIQdNmg0/OtSpUAwVoLoorWdAAxFUpo+mROUG9BuK6yLw8M9ctavojR59LEFhzUtNl4jil+bpvj+fyE2+CDlfJjYDe6X+VyNwfUKVUDXFMJhA11TGC0Jzs4J8jVBiwVSViDYnpb3ZNeX99C4Cbk6TJYFjUAhYSWo96ZodQv84aULhA35OLdYx46aGCFJd2g0fHRdByFFb8spBEJRiVer/N3PT1Gteui6gmFomKbOU9/eQqMRkM85vPj8GSpVF4ScWMQTFtlslT88f4Yf/dlOrK+RPuomOL1ZzYrbugQ+QnB+psz2NUnChoqqKKzviFBTQrx3ucSJrM/tpRLbumwG2v71clCXl9ukOSj0L/I2WyyFb6+D/3bu+p6cS+UFkKnKm9Ty0jWVuG1QdBpMF1x6Ut4Xmva7yxQjjusTuw4bXlUU9vbDy6OC/Wu5Ad9qJSjYmBScStjUfBBesEI1u4ROEwmL82cljy4cCTUN0PP5Gpatk4hLAY9oLiwrbWmSSYvx0RwD69PMzRQIa4K8q+AJ8F1ps1Io1IiEQ5SKdT4+Mi4ByUvn8Bo+rq6DEMRtjXvu7eeD94ebwBTgtjt6+OXPj2NaulTs52rohouqqdf1Cr26DDXM8y+eXSFGsG2D2Zkyly5kSadtqaJXZGTjkr9oIV/Dsg0K+RqapuI4DSbHC6zf0kbZ9floRjBVhe8NXvk+hBD0dMdoj+pNIOx5gmrFpZivE7I0wuEQ5VK9qdxfUgWzpNQ3dT46Ms6e/UO89vL5JghThMJtd/SiaQqFQo0f//RWCvkaU5NFYrEQm7d28OH7csw/O1tmYEML01PSKgkFRofz9Pe38MmRCX7wZzvxffj7vz3OI09s4M1XL6xQWquqghGSaeKtHdKEcakL7y7ns7aEQYGx0TzbNI14T5I21Wd2tkwuWyWVDjc7UQGQqwtGS4KeVJRy1ePI4XH27NtItdpoxq9GoiEano/r+kxNFrn9zl5MU+eTI2PcclsPL79wRVCnqDIH/b4H1nHwg5Hm+Tk5UWTb9k7efetEswM9PVWivT1Cd49KV1eUzFyJy5fmeev1CwD09ibYde8aWlvDTE7kuXxxAc8L8P2AA08P0dubJJ938Bo+9XqDlnQYVVG4dDFLOGLy0eEx7ryrj5OfT6PrKoqiMDaWo6XFprX7xvzFfF0QNlYKnxQUuhf1RXvXwOUivDcpe2IPdikrEtju7FA4moGzOVgbE9j6FeX7dEVmtretEsH55oTgzzZyXb7qhgQcn4dTFZW9Q0k0NcXEgsevfn2cJx4dZN+Tmzj6yQS7H1nP229dZD5TQQioVFzOnc2w/8nNKAgCK3SD96+wo3Xlv1Q8Aa4E4U+tla4dS7Wa40eAQCgK33oojtY6QPnDv6J92//EkZogYkhO9NJHK4QMCUmlLOYzDi8+fxrXlb7QuTqYIY0fP7OJ1v5wc0NRdAWnFgSHZgQ7WxVsTXqvxg3Y2KMQAOdygs6IYPJkhVhITtgEkh//2SLF55UXz+H5gnxdSmCNkMZDj23i5VkVIQQPJhr87f/vGPX6FeqdgkIsYTEzW+HEsSkGBlqoVK6It5pc9qTN/FyF6akyA+u/PLf5n7q+8eA0FArR19cndyTf8LINjfXtYc7PVpkqB8yOBzw9sNTZU9jWHmJDS4pPxqscGq8yXq7yiBewsSPyr0LFX/UU9Hg7gbIStPiBaAo1dE16gDZ/doPftxzwTVelefZqta03zrGxAk69waW5Kjv7rqNiWKzWqMF55A0sW3avC04BusIK93XB+5OCJ/pYwdmElYr8iTmHnnabnp44g71RPjEUHHfpzsySB9OKKMIlkLpUiYR050+3hlFVUDRtcRS6rFts6dhhA98XxGLmYhdVIx4Cx4dgEWQmEhYXL84DV3xA9+zbiKqqNDyfaMIiGbd489XzzC6zDYrHTaqVBrWaJ22YVAUFKV4Kmcp1vUKXl2EYzCxUaLg+y0dzpapLV48ctTUaPiFTdjIajStnQiJhcf6ci0Aa4WuaSma+il+A0YLs5jy8SqrNUnykXDw9NE3B8wLJE92zkUMfjkh/14SFrqsrVMG+CJrg/dChMfY9uYlCvkapVKN/XYq2tihvvnaBQqHGR4fGUDWFZNJiy7YNlMt1JieKCCGYmykhfEEkIkFB4As8P+DTT8a594G1vPv2ZR58eB37ntzEW29c5N4H1vLqS7KDqaoKqRYbXVd45PEh/IaPpuhNj9alulrhXC44dLWF+dXvTlKpNPC8AF1XsMIhHtyziUnd5lxOsD6pEIQ1nnp6My8+d5pXXjpHJBoiHDYYHV7gvgfWgoAjh8YA+d/9T23ivXcuc+LYFE/s3bjo7+rQtyaJosBHh8fIZqvN79gMaVQrNZ7Yu5G/++tjhELqYiyq9FX98U93smNnN9PTJdraYsTiJh2dUVzXAwSDg620t8col+qk0mGiUZt6XfJxY60RUERTrFapuPi+YNddfZw4Pk2t7rH7sfUc/GCETZvbCUdD6Ao0rjpFXxgJOLBW8nV/d1nwww2L0vFldWpBdu+6wgpRQ1q63dsF3aukJW1KwbuTgpmqBEa6Iig2oN2W4PXqkpMfOR5fXgLBqQWYrQpqHtzertAZls9v+ILZ6RKq7/PZJxPcsauXO3b1gSL4zjPbKRRqZLMVolGT9g7JyQ1HQ4TDX56S5AWCzzJynH6gfyUwhdUdP2oeWMLhxd9f5lt7f0bj1f+ZwUf/j3TFdYqOj7t4719yZ1H0EPF0jL/+uxPUl/0eU4Oo6vPqooJ+aeMbDyns6oCzOcG29LX8X5AdbkVRaLTa6IpCbJkFaa1Q4/SpGZ44sImQBk61QTJlNcf16wGvHvD58XmqV/vEIsW25WKtaf5/dQlB01YukymzfmMC37/RqvbPV994RKYoCobx9Tek/eeqjrjJ+dkqaQvm/YBj84Id6Stm66auct+6KP1JlVfOV/jl2Rrf8gK2dkcxruPd9nWrpYjO5TGcRcfj+IQEOOP5Bklbp1T3sQ2VXPUKQX/5eB9kx2JrC/yXMwG7exTWxhUJ+GoBczMlFrIOsaSFSETZ1SEvNy+QStM2W+5uNVV2Y89MFslVZS7y9ew9KnWPY+OlZh8y8SVSp7ojClVP8IuLgt09V0QAV3cSGoFgJGlz9x09fH58irvv7efwhyMUCjUWTQWwLZ3H92zk0IejK0AqSNAZjYcwdJVSscaDDw/y8ZFxEgmr+TuWBDlHP5lg/1ObOPrpBEKmQKMguXCurmKZ8neXildiEDOZCi+9cJZnf7iTgaFOhBAcOTwlgemyBTwcCVEoyBux7wdoiooQkgHheQGaqX2hL6mqKMwvOPhCOgLUfakW11yXO+ImpqnjewJM8Bp+0+bp6s9EIDsurekw/kSGTZZGRzqGyrU8wgBobbd5+rtbmZkqkcs5KIr0hzz04SgL2SooUCzU6OiMrlAFp1JhPv1oUvJUi3VmZ0v0rUnS3hnls08miCVMHtw9wHymQj4nR6ytbVE8z+PwwbGmwEtbtIa69Tbp91suuVi2Ti5Xa4648zmHrm456l/IVvnxn98qc9DLLvFF/16E4KXfneLZH+8k1XLjWWFbi80bL51jerq02GlXUBT52u+9coZv//AW7u5Qm/cgNWzzw5/cwvRUiflMmWhUukUUCg5vv3GpyadVFIFtGzzz/R2MjeaYmioxuD5NujXMyRMzfH5ihljMJFg8CSxLR9NVZmYqCBHwF3+50i6qtzdBreZjmQZ9axK0d0TILXKCW1sjfPbpBOnWKJ+fmMa2DXbe2oEfKFTKLqVSnUK+hmlpxOMWbW1h0mmb2RnZrb79zl7S6TC//uXnzGcq3Ht/GAKB6wqUqy7xjAPjZXhzXBqw21dNRARSnDlbhZmKBKrPDF77uOb1oivs65c/KzcEv7oo+NnmL76XT1cl13GpFBTSlmAwfu1rGZpCo+RQ8yRI+uSjCe68q6+Z2tXZFSWdtikWa5RLdVrbIkTjxg03/1eXrirc37X6z5bHhi6VLwQaAX4pQ6PuUqaFlqf+F+an5nj4kQFef+X84n3jilBz75ObqFccYmpAzFKoB0La0y3SAFbb+B7LSC/W1YDpUgkh6OiMEQpp1F2Pmi8FrgDFOYfJhVH+4me3YC26Xywl0CmKwuRslXzOQVNo3s+WluJw2CCfc6iU66zpT6362jLiGGKxr5db0TcenDYaDXK5HKlU6iZIRSYbLTbIeKBLcK4Ef3VWsCEJg3FImlJx2ZsK873tBv/1swKfTDeo1PIMtkfoiIe+9mlbJyfLFBwPTakQs3QMTSFTbuD7Pk69gW2GyPhykawtdsSWVPpr0tfSGHZ1KAwl4a0JgW0o6IUqf/jdKaqL0Yq6oWHaOvsODPGWa1P1BElTIWpIg22A1mgIK6RRd6VJ8h3rEqt6DxYcj4Yvmpn3qS9p3r8+odJmSX7Z/rWSXnB1J0FX4PYdnbz9xgWy81VyCw7f+s5WMpkK42N5YjE5Oj11YoZKxWXP/iEOfTgK0PS4tGyDRMLi5RfOUK/7fP+HOyRPb65KLC5VzIW8w+139hKLh7jl1h4ycxVEIGSnGYjGQjz8yCCHPhxp/vvSfd22Ddo7ozQ8H0VRSKdt6dG67KOqVlwSCQmINE2OSRUkOPX9ANC+0IvQ83yMSAhfQMWTVkbJkOS5nj45w979Q3z4vhQ9uY2AUEjDtg0e37tRZogvlQDTMmhtj/D7X58Erqh6V4vrrDk+b756gQ1DrczNlti0uZ3nf3daCogU+X4MS+PRJzZghfVm/rZpa9x9bz+/fe4MmqHRFg/hOBnWr29l5209lEs1KmWXvjUJImGDWt0jt1Dl1MkZFrLVpvNANGZSKbsMX87y+N4hfv/rk6iK3EDNZyq8/sp5nvzWFk4cm2HzVmnG7/sBlq1hmjanT82wcaiDTz+dwHV9pqeKdHTJRfdqKzCQpv3JhMXlS1diV5d2GpqukpmrkJ8tEV9/ZbEPAMPWGNzQQjQWIp22cevyd9/3wFpKxTotrWHS6TALC1V+95vPMQwVzxOUijXuvref0dEcqqqgagpBIJblv48QCmk88tgGfv43n7FhKE1HZ4xSocavPhzloYcHSLXYvPX6RR56ZBDbMijWa8zNlunsijddEEBuIn7y392CEjWxwwaapiACEARUqy4iULBsnaFNbVTKdX73m5PMzZaJREO0tkWp1RpMTxZpa4+syJZvs6XQ6dkNq1sjKSjs75dxpN0RwY+HlBsCo+UVNRS+M3DjxyyJZrpWYXR1ha8jABIQTdrNbmtuweG1l8/T3Rtn5y3dLGSlpZQZCZFOWhim+pWA6RdVs3O4bERfakAMF8+t0tqRhEgHv33+MrXyZdo3bub+BweoVKRLSFtbmLaOKGgNzp5cFu6w+N6W19UbX8dfnRpxdZm2DBT51e/OAj6xkHQRELrG9761CdNUr0mgO54FL1ulJWWDIqkAQiylfEGl6pJIWpw/J3nwpqk3edpLpWkqiqrQ3v71GenDTXBKEASUy2USicS/9KF8LeriXFV2sRSFhGVwf1SOhUeLMFKCyRlpdrwlBemwwZ/tiPPLU2XenfKo+2UmcjoDbfZXTjz656xaw0eg4gnIO/JCFagECLqiYEUsPKFSawQ4i4p109C4fW3yuvSFhKnwvUGFQs7l//tXR6k5jaaCaGnc+dIL53joW9vpS69+2fW12FyY8al5AZ9PlNjSHV1BIQDpGjA879Dwgy9tyr38GPethXenBN9at7KT4HkBIpBdDGfxM5meKvHbX5/kib0bGFifplSQsY0bN7fx6J4N1JwGO3Z2EY3JxVRVZbcrGgtxx641vPzCWX758xM8+8Pt9PYmmJstk89JFWpra4SzZ2fp6knw0KPrEX5AtdogGjOxLJ2QoVEs1PF9gaIINE3FDkuj+iVbGSEEvd1RIpEQpVKdYHHRLBbrhCMGlqWjLvkDLgJUTQFdV1d0HVcT4QgRkGgNY5ka5bJPSF3MyRZykbtwfp4f/uRWMhkZbWrbBr19SZ7//SkJTha/e9vW+cGPdnL21Gzze5AG2tfyXpe6O5ouFcxnTs8xl6lI7mNVenamW2xicYtyqU7Akgm60lT/PvbdHQT5MpWC7I52dMawwjooV4zvgwDefO0CuZxcrBsNf9ESa6jpr1mtuExN5Hl87wZUVaFYqBFPWjTcgPfekVGex49N88jj69ENlcnxIm0dUW6/cw0HPxhhZrKEEZId6rWDqWV0hausd/ZvYmIstwyYXinfC0BTmJsrM7CxhcayNrcGZDNVjh+dxrRUNm/toFx2mZ0p4XuC4eEFgkBwx529PPTIIBNjeWxb2px9dnSCx57YiG6ojI/mCYel+ObwwVGKhfqi7dYkP/zJLUxNFBkfy9PRGeWhhwcol11OHJvirnv6+cXffEYsbhFPWNx9Tx8h02D3o4NSMBU3ae+IUSo3GB/NsXV7N4Hvkys6WFYIEShUnQaFXBW3Ifjko3FmZ0pEoiG+94MduHWXfK5GPudw8P0RHnpkkM7uCFVfkHHg32xeqY6/unRVoSciOY9fdZ51NS/+6jqeEXTY8FVs6qbKPkYyihnS8Je5d4yNF7g8WiCRstl9YAvHCwqPm8pXFoHdqFSkKC8SCVGtNNA0BUXXCGsCUcygKbD9nm28/uoFAkfytOdHRviw0CActYnHTbbv7ERoPoV8mVTKav7uqgeJq5a6qze+pirtq76oAiDWGuanP9nJwpycnJgxm7W9MdkxverxZ3OCrCPYkAxRr2lN4KksU66WinXiCYtw2ODEsamm0G8JoMrkNJ37HxxA+5qhwa/Z4dysf+nyFjuGHQmzaReloLA2Dmvj8m/zjuD5YcHPNkNbLMR/uCvFu8MOhyaq3JIOKNfLdCdNUmEDU1eJWtoKhfq/ZE3la9Q9+R67UjYzi6KRta1h2qIahdyCTC/RDQSCV0cDHuqWdIYveg81x+fCuQy1peSfxRvEkpI7ranUFsooratzHTsTMsf+/HSJguNxdrq8gn/qB4K5ktv8jiLmVzdQTpkKG5PwxgRYMw5uIDAWR8VtHVGKhRq+L5p2OXOzZV575QJ33bMGRVXo6o6jKIIgEBw6OEbgB4QjBp+fmKGYr7HnwCY8X5BuDbP/qU3kczVOnZxjw6ZWWtJhYjGTubkyMzNFNmxs46XXLjKdcdj3SD87buni978+SS7nsHZdip/+7Hbm5spNo/re/hSnajpHxwJSpsKamILt+jyxbyOvvHiWublKUxT12aeTPPujnbz6svQi1FSFctkl1WJz1z391Gselq1Ruyqzursnju8LGSFarPP44xt4/ZXzVCoNXEFTAXv3vf3YEZ2+aJKenjh/+N1p5mbL7H9yM5k5aeXSkg7T2ia7vGfPZFZ8D1eP/xRFQdcUcgsOjtNYtJCBbKbCyy+eJRY3CYdDjE8UQAgee3w9v7sUcFubzEWveILTWcGlosrt7Ql2bkg1wbYMepAj26ihEEuGmhY2+VwN09RQVbVpiRUEgpmZMj29SZ777Sme+cEOMpkyW7a14zg+d9+7hlKxTqolTMP1OX50kltv60EzVGZnSpw+KYG4qipEoiYICZy/98Mdi68pgXNnZwyn1kBRrw+fhIBIJITnBhKRLla14vH735xi67YOPnhvGE1VueX2bqJRk7nZErG4SVt7hI7OGAu5Krff0Uu+4DB8aYGOjhiNhs/GTWm6uuJcupBhZrrE9h1d2GGDzz6dYPej6zF0BVVVGB3J0dYe4eUXz/LAQwNcvLBAOBriR39+G9lshWK+Tt31scMhLFtnzboUhq6CEGTnK3R1J6jXZDqUU2nQaPhYVghNg761KSYnimze2s79D66juzuO47i88vIFbrm1B9PUWchVpePDf7+Ln4+rPNRzY2AK0iB/3hGgwO8vC1RF0BOBnqhCR1h25FSuFTQtnSM3qnhIoXg1EfaGJXhlDA6s1Tjw5GZeefEsecdbNO+HiK2xe/cAMVtlYTbg5ALMO9K/+PY25RqerBsILhVWul1cr5ZTl3bdvQbfCyhXPFRFIZkIoQQ+dixM1REEDRffk/fuoF6FhkuhpFIpu0xPFeleE8Xzfdo7U81JgBArbbhWM+FP2wpHZgXr4l+8BoZUCEU0IgMp+pXUihH+8pquCM7lBHd1KqSVGG+8eqHJOa8v84m1bYP29gj7n97ML//2GCc/n24K/UqlGn1rkpJ/nrBxnAZCfH2mxzfB6c1qVsFpNKl7N+KPttoKLZbgYgHWJyQ/b/dAmIFWk1+frtKu14A6U3nJu1MAO6Ry65r4vygvte4txYYqxGyD/tYw61rDNAJBzVfQ1avSSVCIhlSydehdvGF7gSBbh46rxjSqqjI/VyS3UF3Bf1yqIBB4rk+9VCPngoZY1a6kI25SchpM52vkqle8Tx3X5/PJElVXJnYJlKb5/letzSmFLS0KI67N8UCOsINAeunFt3Zc46W3PEP8xz+9jTdfPU+p4rJrV19TLd3WHiMaDaEoAhcV3dRp74hiGBrZ+QoTowUs22BiPE8gBOvXt/Lm6xeoFeuyyxAKMTVZkN08YGQ4x8iwBAWJpMX5CxnCEYNdAykCoTBeAtX1+fnfn+Q7T23kuz/YQTZTJZORxvKtbTJm9dkf7SQzV2FiokBPTxxlMU7y48MuT393K8//9lSzm5dqsanXfA5+MEIQBNTcANPUuPuefnRdYXa6RCxuMbg+TTwZkmNHIdBDKg/uHuDlF89y4dICkVSEvo4w585lCBSFc6dnVxgeLFU+57BOSVGtSqBaKbtEIlKRH42ZK0ZwpWJdWmrFTFQ7RKw1ijMj6AwrvDQa0AhkpOpTayFiKNeM/z6eE8xWBQfWSscJw9boG0ixVlWYGMnz6kvn5QMXO6mFfA07bEi1+0dj3HJrD7/++5PsfmyAZEo6A0xPlwiHDTZu7pCLpyf49KPx5muGQhqNuk+l3MCM6M3X7FdSKAJKxQYjl3J0dsVWHTfCYsxoe5SZqSKdaxIIIVBVlcmJAvOZMuFwL0LA6ZOzvPv2MOs3pBna1IqiwKEPR7n73n4unJvjwd2DVCsNxkbzNBoBszMl5mZK3HFXL71rUsQTNplMBV1X2XtgE6GQSqFQ45OPxynkHQxDI5+r8fGRRe/Tty4ycjnH+vVpbrujh0BAfsFBM1R0TaXRCGi4HpZp4DY8DFdterHWazKyMpEM4zU82toirF+fplp1ef/dS4xczvPQI4N8dnSCnTu7KRTqCAGj4wUeXdNKX2SVLnMg0FQ5zj0+LziXh0d6rwiSAiH4LCMYLgg+mJLdPkUBxxdYmvRIjhiSmvTjjaxytl6pbI0Vop0bl+D1cUFEl/zXW9pt9n9/B6MTJdTqle6+aWt8OCPYnFIIqVKkpSkK9VVm+ypga3B4RtBmwfZWyVUv1CFTkxOCakMKyQatgN89d4a66/PJ8Wke3z/Ea4udw0KuTCrWSiLlEQQCpZZHNSwIPAg8glIGN9ZDQ1XJ5xx610r3CcOiOQkIB1fO2euZ8McMKbxqBOJL23XJTeXqPwuE4JUxwXcHpJWXiqT0HD44yr4DmyiW6uRzNdJpu3mvAvjJz+7g8sUs1YpLujXCwGAL+bxDKhXm9Mlp7rpnzc2x/s36etZyn+PupBxd+EKgLesYTlakb96mlMLZnEzcabGk8XF/XGP/UJSTswaG6WKqAY4b4PkBVTfg1GSZnX2xfzFOamUpDhIFMxzh+Dzc0a5Q9eXivfsqMr0XCAbjkrv1440KYUPeLE9nAz7xBI/0KoQ0+GQOHuyRIC6RvL74o+H5JJMWH80E3HfVawVCqmW7IzDQHmEqX0NB4dRkme29MS7MVnAaMqGqM2mRsA1aY6tTJybK0gbl2lGSoOFLcYIQgu6uGC0RnXzOQwhBsVjHtHQsS6dSbVxDpnJdn1yuyr0PruPVl87x0guyo2fZBqMjC9z3wDpEAIrrcrphsSMW0BHSSLeGycxVWMhW6Vsk5b/z9iUW5qskW2witkaiLcbMpblr3ktmTkZHwhUulxAK/XGF8csFbAKOfjrJPXevIZE06e6NUSzUqddc6vUGqqri+wF33NnHxMQCf/jNGWJxC1SFi+fnMZZxIbdu6+TlF8/ScH0SKZvioojrD8+dpq09whN7NnLwgxFOfT7D9364A9PWqDs+EzMlajWPA09tJp+vMZ5xWNsZZtv2Tl5/9TyzsxVChkLI0Fac+6mUTTZTa1rSANz/4DqqlQYnjk9d6YQsnreqohCLhnhw7yYO5TQGYoLXxqX9z/7+62/6LhYEUxXBzvTKjpsQYrHLHWl2ghRodp+PHBpj/5ObeP+9YU4cm+K2O3oo5GXHVNc12tuj6IZKMmlTrbgc/GCEhcXNRZPDeWgU3VBZv6WNIAiai2694nHq82liMZNKpc6+Jzfx8gtnVwBUy5KpYzWnQXahSnd/AiFADQTzcxWCQPDRkTH2Hhjiww+GaUnbXLwwz4Xz883X//CDYR57fCMH3x9heDjHvff18+EHIwSB4MjhcUxbY+ctvWi6Sks6TCikYtsG5XKdF547Q73m8cM/u7VpGzUzXebjI2M8vncjqqZSKtZZyDn09saxbYOpySLjo3nSbRF6ehOoirRHCoV0SiU5pUm3RvD9gJojvU4tO8TUVJHMXIXevhS9fSlOn5zhttt7OfThSDPutbBQZet2hYYnr8tcXXA+D9mawAukGEZToNyAjUmawHTp3Lm9Xf79pdGAtXFYF1fQVYWFuvQtna8J+mJSsX8jnWVHGM7kVv+Z6wvqgRQIzTmC96dkapHjCdK2HF1fclT01iQ721d2Bu/phC9DFdBVOclrBIvJfEV5zF0R6YQRNcA2IK0rLMyU8Bo+hgrz8w6fHJ/h8f1S1JSfL9G7Jk5XtMB8zaIRamm6axhqgKjOE1Zcsp6FGrGb1JMgEKtOAjquE5gggFhIbh7/MerTOViflMAUrhj6P7ZnA7MzJUINncEt7fS0h1dwd2dcl4ENaaYni+Tz0s83FNI4fHCEe+7tx7aVmyb8X6fSNI1kMommfX0zZv+5qlRb4l/S7J69PwXttmBzi+wkmqq0CBkuyt3p5/OSA5W24cm10mjY10LMBCG+tVZ6sGUrDc5MFsk7HmdnKmzuit7gKP5pquEHjGXl4hAOaZghjQtZqcKeLEsPUFVViUQiqItjxpoPp3LwUGvA7GiRQs4hlQ5zf0eUkqrxwkjAI70KE2WB4/iEIyG8hn/dLlAkbBBP2NyTUInoV24CE2XBsXlBT0Sh3ZY33609cU5PyvSbExMl+UBF45Y1iRtaR4FcWD6clubPaUvmTp+Yh88y0ow74whMFe7rljv95397inJJdrmPHBrjwd2DfPj+MDPT5eZO2rJ09h4YAgFHP57g29/bxsJ8lYWsFDq1tIQpVxt8+sk4lXKDbz+7g0NZld66w9j5WdraIvT0JXnphbPMz1dkOpEiVaVPPrmJizWNzsRKYF8PJJBRkBsnK36Fy6UqkF1wKDYExdEid+4SWCGNWq1BOGzgOA2Kix5+Pb0JarU6Lz1/TnaIS3XCYYOFhRq2bciOZVR2LJe+t8YiFcDzJWcvm6mQmb1igD83UyIWt1cASwArZrH3wBCKFzA9V2ZysoTf8Kg7EiAs2UCFQhqt7VF++8vPVzz/1MkZnti3kddePo+mKuw7sIlyqU6xWKOvP0VPbxzT1jgxElBwpYBluHj9BWV+ESB02NdaEi3Vcguret2TAjMk1cOydJ55djtjo3mq1QaWqVGveZw8OY2+eJ3cc99aotEQt93RS8dEYQWHMztfIZutMqRKrivIKcPURJH5uQqD61t5+YUz3P/gOvbsH5Kxqcsy6NPpMK++fJZd9/QjPIVisUYxL9NygkAwN1vhyKFRHt69HkWVHeZINEQsZtJwfb71nW188vE4Rw6PY1k6pqXz4O5B8gsOpZLs+Ksq6LqCrhukUhbj40Vmp0vce/9a2juihMM6e5+UNIhSsU5re5TOriiVch237hMyNEzTQFGhvSNCNBaSoRGGRr5QJZd1aDQCWlpsLEsmazlVD1VT0A0NVVXo7IzKGMuRHKqi8ODuQZ7/7edksw7KYrctnQ7jB4IzCwLHR244WhVubVNW+Jy6geC3lwRbW8SqCv2QKsFl1JCArsVUaDHhy3JI18TgzQlglXlA0YWDM2KRCw37+hXCusKvLgo6FwVUCpB3Bf41xNKv1rTYkFTY1KISiCs0pOWlqQrHzjloKjgNKRBqlBw++uAilreAFYK2WBd1K4znerh1bxkXUyGR6EBU83SluwilopzJQf/i+rAkyuv7gvE7yHvXXR1LMuMb16wTkKuBpcnGh0B+po4nNyO6InmuMlTgSi0/nj7g7y8E7FdXMGHoSdqcnynT0xnDjBjUSnU0TefxPRu+dsAUboJTdF2npaXlX/ow/sWr4QeMzDsIVBJho8mv3NUBf39B7n5jITnSB+iNyJ3r5SJsSMLJrMALBFta4NSCfJ7jyZtjazTEmtYI+Yq7QtTwz1nHxopU3AAUjcH2CKmIVIb/3XlB3IDdfQqqqhGNxprPiRsKO/QqLz93Ftdx8TyBritEIiH2PbmZbw3Y/PpiwNa0yuhwjo7OKC8+f/oa0jlIYcyBb21lfCxH6+ZuRosBhTpUGrLT0GnDnCP4cFqhzRakbYPNvXGmczXyi8bJ69sjNwSmbiBv+GlLYWerTCD5ZE6q8LsiCn8+pDQdALI1wcdzAbZu8YM/u4Vzp+eYn6+SSFgoCjy+ZyP1uifz1+MmrW0RKqU6H74/wm139PLyC2fZdXcfiaRFujXMJ59OcvJCnnhIxopm50pEIimyExUmxwtMjhew4znue2gdlUVbnZaURW9fgo6eCO+d8elLR1eoug0VhC87QlFbw2qJ8vfnfQLANhTWR21MDUxV4dgnE+y8tYdioSYFTiq0tkVw61Ix/vabMqu6VvdQVdkdjCctsvPlxe/HIJ+/4gPoeTLez1tCx0AuX8O0ZKJNdsHh1MnZFcCy6gnyWYfZqSIHPxxFt0PsOzDE6y/LcyEQomkDtffJTRQKzjUK9tyCw+lTs+zZPwQK5EouvV1RdtzahR5S8YVMGsvWwVRhfRK+O3DtoucFAR/NSsshkFzD9dfRfC63sLp0IQtCejsahsbrr8mO4SOPb6Cry0DVVLLZCj09SRIJk1RLmA/fH2ZgveRvfvDesAwOWLTS0jSFdGt4BRAxVMjOVygW61imxu139HL00wk2DrVjmhqJpIVtyc7s8WOT5HN14nGTSrnO3EyZ5393mp/+7HZs26Be95iZLvP6q+d57IkNnD45S6rFZt1AC7GYyfDlBeJxmz37hzAMjbfflFZTbW0R9uwfIp6wQBHMzZQ5eHCUQqFOd1eUZCrMxQvzTE0W+fYz2zj1+Qy2rWPaBrGYyX/9z5/ws393O6lUhHK5TqlUo+EKqtU6qqZSrTZw3QDhC9b0p5iekilRXd1xOjqj2GGd+UxVOgu02OiGQjFf5dKFee7YtYbnf3eKzFwVTeYZYNohWrsT/Paiz/qETGe6tW117ulsVZrgX2+C/GCPwi8viKal3FetUmMxbarONelQLbYc+/9sswRjAYL3pgStFoyXYNYRzDmCkis7vF8luW6plnszL3HFV+taBgISLTZlGYhEfBGoK0BN2Dh1lWhnL28fuoymKux9enszdUwAxYpPR2cH+5/cRD6sc3Y+YGtrbMWx3Gj8vlRf5XM+OgsDCanwX6jL95owZdTsppSCrlw/+GDpeAAiumC0JG0Ol8oOadghjbOZIm0xg+07WheDBcTXDpjCTXAquWW1GpZlNTtm36TyA8Hx8SKlmi89GYGt3VcuwLAu/1wowGBcNEcJIOPuNiZlHvqhWWncfLkgF583Jxb5b7q8kVCoU50pkUrZzOc90gl9teTNf5Iq1Twqroxl3dwVbdovrY0rPKoIzuVk90AIQcN1MULSDqvu+LzyhzNk5iooyy7ecsnlhedP88M/u4XvDqrUaoLjIwuMj8Gjj2/k9VfPN0nnhUKNVMpiw8Y2jh6doKc3KUG7BqYN9RC4gcxX39kGdQ9mHBgpwqakwfbeEEWngaooRL+gY3o+D6NFwYG1Cn1R+SfjCFIm19hSpS2FvWsUsjXBq9MaD61r4eyZOc6fm5PAQsDQpjb27B8iM1cmM1ehWmlwy209HD44iqJAyNDJ5MrSi1SXSUxLjZp8zkGLtdDZZnOy7uF5glrN44N3q4TCJpqpk50vkW4L4wvYloaKqq1QdasoWNoy66UWnd4Wml2ZhhPjhCUTlSYnimze0oHnSa5frS6tpqrVBocPjpKZKxNPWAS+wFcDwmGLSNRsmsEvFyGBVPQ7NW/FORpLWJwfL1F1IRoJkS/WqC9yPZ3F7PDulhBOqU7DcVGCgHOnZ5vdz6WO4LrBFtLtYY5+NLnq95hbcPjgvWHuuW8N5vpugpBAMSTF5kJedtmfHVwZfVv1BJ9n5f/PO4Kqp2AZCmFD8J0BccMFDaSY7/nfniKRtLj73jUoKPz+NyfJLCb3/OJvPqOtPcKTT2+hqytGLjfHh++PsJCtsvfJTbSmw7zz1kVmZpa6/PI/dtigpzdBEAQrXivVEqZ+eo6ZmRKtbRF2hLoJAkEm75BqiaBpCu+8dYlSqcbjezdQr0nqSWmxu/3+u5f5/o928pu/P0F50dT/s6OT3HZHDwc/GOXs6Tlc1yeZsrlzVw9btnUzPVVk+86uZvRoOKxTLtUoFn05di/WURU5up8Yl1ML29Zpa4tw4bwMg7Asnc7OGJ4XUHMC5uaKjI0uEI9bbBxqxbIj5PMO7741zF339mFZOoKA7p44kZhBw/Vxaz6e7xOLm4Ds+o2P5Gk0BPuf3MIfnpOOD5omZde2rfP9H+0kEtP59o3zOQCa1/71KnQDP9AvqtlqwOFZ2LNGWbEWNEvArW1XuoR1b9H6akYKnHakFe5ok2r8lWNugbNo2Vb35dRKUWQQwPKu8GopT3CtPVvTmzQZI2rreMtCMnyh0hAWqqYST0aYLyk4Pngn59j31JZrrtVUq01CBLw1LhjL1+hLmP9k1LQ9/co/WDw8WQrwBGxKyr/Pl+qMLzgEQlLbVBaDZYLV+rxfn/rGg9NGo8HMzAw9PT2Y5tfLhPafozIll2JNWiuBVK3rK0RLCt8ZhEsFOa6petJkeUta3jQ+mIZOW5A0JcB7LhsQD8kb5LEs9PpV3nnlLHoQUPd8XF8QCqk8/fRm+rrD/yw7tvlFABLSVdquMhpeE1P5eC7gQl6wLuqRy+dIp9MYRoiZqRKZRW7b8o8kEJLzNjNVYt2GFhYm8kQiJm++foE77+xj35ObyMxVUDWFjUOtxOIWb791gfHxInff248ZWT4OW3kjCoUkP4llXa64/eXUB9taFPJ1OdK/p1OOkb/IXy9tKXxrnWC+ZrH/6S1cOJchn3NIJCws2+C5357k0cc3MDK8wKnPZykW69i2zp/99DY+eO8yG4ba0XWVialCM0UFJJ9S8arUPJ963W+CCxSFpB+gVFU8U28qW9fFZQ741oEv5nI1AyHslWD2008m2LK1g8OfTJJIWowOL7BlW6e0QVrMkZefscaBb23hrffHqPqCsCaN0sOLOdau66MbmlTRAwgIWTrhqEndcWVAQNwin3fxPZ+GLzvqiZBGxA5JPteiQf8S0IxEQ9i2wdxcGU1XaGkP39icXoFI1KQ/IUd0Px2SPlYfTAv2rVHIu3B6RgosHE9QcCXvcDChsCUSUJovU8w4tKZtFDeGusj3A7lwL1miLQmnlmzFhICRkTxWSOW739/OQs4hm6kSj5u0dUSxLJ18roqCwq67+mjvjGFZOg3XY3Qkv+It2LbBsz+6pWn9tfTaExN52tuj+F6A43icPjXGvfevxXFcNm/ppFBwyC04DG1uo7s7wbvvXOSee9fiVL1mJOvZMxnssM7/7i92MTVRZG6uTGtrhDVrEnR0xJibk5umdGuYtvYomUyZRsNny9Y2LMtgZrrE9FSRcMQglbSIRkPYloFTk6JQXVcxQhrP/nAn7797GbjCo/3s6AQ/+PEtHD40QiFf49HHN/C3/+0zPvloggNPbSIcCfHQI4PMzZYIAkEsZrGQq6KrCp1dcWxbo1bz8DzwGnID1NWbYPjSAtlsmW8/s53paUl7SLVYrOmNkYwH1P4R8dDaL6Ecv7q8QPDaODzRt5zPLii6EA8BSGB1y7JIUVtXsDR5jt3VqTSFpNqy3/nxHCzUBBFDwdQk7cDS5cTkWEauLdtapMd23QmuAaaw0p4NRYrTUBQaNY99jw/y+qvnZBqSL5qevk8+tZFLF+ep5B3CMYuJOYfyKtdqqs0GodBrecxnc3RHW9H1fxpV+42BqcANZMe53JDiN9eXYquMI6kemgL5OjzaK2kN+WqDs9NlOYLiSpBYZ/zra/W4VN94cPpNr5lCHVCI2wbrOyJEVsl1D6kKm1NS5d0IpBL09THBQl0Q1mFtTKHagNfGAlImPNanEDUUGo7Pr38hgSmAqWu4vofr+rz0wll++ue3Yt04PKZZpZpHOKQ1F9W6F1BvBIRNbVWz+uVVWPTtXHrckVnBRFnQZkvz/P39Cq+NC47PBdwWhkpR0BGHzFx50ebn2t8ZCEEmU6avP8HURIFwRBocv/bKeXbdvYZjRyeJxkxOfT7D1GSRaNzk2WUenf/YJRCcmIf7uxTO5QWHZ2Rc4ZcrhVYL6qEQG7Z1UspWmM9UFnmmm6hWXcJhk42b2mUXeFMb46MLbN3WxUcfjbHvwGY+/HAEkN0929RpbY/yV397grakyZ59Q7z0hzPUajLicWHBoaNDCoyKisZoRgp2NFVSG9q/JJdrSQiwHMzGkyY/+LNbmJwoMDaSQwjBT/7N7YyPyaSfRMKiJW3T1hbmsYf7+flvziLwcX04dHSa/U9u4uD7I5QbAcmkRT5fwzB1Ht87xGcnpjFNjaef2oRwXSoFh2AZ2U1VFMK2QW9vAu0qrt/yyM5kysYPriTCLC20AkEgFFRkZ9gydYYv5ngwHeVoRuX0glyoj8zBLXGf1lKZcsGhPWGzsytKIqZTzFR544Wz1F2fqidVzZ+ZkjPZ3hHGqXpMT5bIzJUXozdjJJJWM9qwWnUJ6SqO4/Of/j9H6OiM0dOXoOY2UDWFfK5Go+GzYShNKhXmzPl5QprK5Yvz/OTf3MZ8Rgrf0q1hevuSxOIGy9nXS2bok+MFnvz2Fj58b5iNm9r52786Kvm0lo5bl/eIO3b18d47F7nr7n5yuSrxuEUiecVj8rNPp9mwsY2WtEXfmiSjIwt88P4o7e0ROrtitLaFqdV8pqeLpFI2qaRNPl+jkMtjhDRa2yJYpka16tLZHeff/vtdTI4XyGarpFI2vX0JstkK/Wtb2Lq9k1jMpF73+M4z22m4DSYnimTnqwgEP/vf38mF8/OMDOdYv6GVRsMjnrAwQyq1mkc8HkLXpSBuZqZMuVBDD+kkEhZCCKYmi5imTiQSIp+v0NUZIzub59znsxgLVVKbktC+88te0JQaMsrzllZp/fQPrUAIXh0TpC3oaEaKCt6ckBSQR3uV69AIBGdygvu7lBUOJyey0j3C8WBHWuGeztWnljvScDYH700Lap7CjqDEQtVDV2RnVVMkjQgkQJ0cL3DhQob+/hZeeekcDdenrT3K/Q+sk2lKE4VFPrPJxQvztLVF8QJBsSg9hOHaa3WJ515sgO+uepirlkCmR10do/rH1tmcFCTHDemsENIkvcIXkk++d43Cq+OCe7oUOhepBOdmyiACdFVhsD2MoSlEFsf7X/e6CU6/ISWEYCTrUK37JMIGnh9Q9wKKNQ+BSn9reFVgenUZqsK2Ftml8wLBWxOya3NgrcK7k4JvDyx6ozbj4lbCipCm4vo+Ts1jZrrE2oFY00h8tWPOVT0mcjUWKg0MTUFTFHwhVnBXe5Im4ZCG6we0x0LXvI9qXXaGl/iad3XIzOh8HT6YEgwkFL61TmG6qHBsQqXNUMnmlC+Mc4vFTC5fXiAaMzn04WiTawqSs5nJVFhIV7n3/rV09Sa45OqoNUHa+scdCS1ZlGScgHkHhpIK71UCTszDjtYv/1qmChVD5bQWZ+1Qgq6wglp1yc5X0Q2VTVvbaE1HqFZdTDPExx+N8cijG/j06CT5ulwoqmjs3TtENudQq/vks1UOHxxt0hzy+RqJpMXatSmOn5hm451rOb+g4gEDcam2hS/H5QJWFSaoQtDeHiVkaLz28nnm5yvEEyaRqEk+X2X9hg2oukJbe5gf/XgnszMlFnIO/R02PT1x+tcmmZkuMpupkGyJ0NZik5krc99dvbR3xgjpCqPDDkZIW8ErDhZBxp1392HaIcS1io8VPohXd369QMH3AryGx569G3nnvWHGZx3Clsa3nt7E1g1hPpoRrBUOHzx/jmrdo+hKbl3c1vjxD7bx5svnaLgyDDa6eBnU6x5/+P0pvvPMdn7368+Zn682uWmmqfP0t7cuci8X6RiaSjiiYVlSfT61KMwDiCdMyRPuiqJqGh9+MEpL2mbbti5GR/OEwzqbtnWQiJtohoIHjJYE/bElSyNp2XX000k2bmrlif1D5HMOz/54J6VSXdpoRU0i0RC+F/DQI+s59OEIA4Np4gmLarWxQnD4zluX2f3oIIV8nY7OOGHbIJdzKJdd0q1hOjospiY93nz9Itu2d+J7AYoKLfEw1YrL668Ms/fAJi5dyLJ+Sxvrt7SxWYWFeYdPP55g+85uAh9qNWne3tkZ48TxKVrbojy0e5D33rnMH35/hr37N7JhYyvFYo2xsTyplE1PbxwjJAGppslIVtf1icdMEgkLkAEDxVyNkKnjeT7lioumKvhBwIVzGSolh76ESunQ3xHbtwYnlPpS13LMUOiNCj6ckZ32pfPTEyvH5F+mJsqCTzPStqncgJfHAgbiCtNlQdSAuzpWB5Y1XwLksK6wIbnyNZMhmK7A/v5rKUfL6/i8IFeDTluhPaywcNYhpMrxv7/4R1MlYPP9gOHLOTZsbOf3vz5J3fVQgLm5Mm+9cZH7H1rH+HiB2dkS1Yr0M+1fl5bTkrqH7/lopt60Arzas3RNDC5MwV+fDdjUKrirY2Wn0xeieT2OlsT/n73/CpLjSvM9wd9x9wgPrVJLJJAQCYAQFABZ1AqSoqpYRVZVd1f31FVz920fdm3mZWxtH3rM1mz3aXZ3Zqfune57b3dpQS2KWoEgWSQIEBpIZCZSi9A6XJx9OBGRGoLVdbvrFj8zGmEpIj083I//z/f9BVdyitZ2e/viutaoqu1SrNr4PdcDiirJqmxDewC2xlaP/V0p2ZnQuJqXLJRhIAyZksVYskTNchBIdnSHSQT/5XiY3kh9DU7/TKrmSMaSFUBFdTZKopEIeYkFPFzJSTatOe5RN1pHgGVxeYYmeKBHiYouptXIoVE5C6bny2Rrsp4vDqahRjY4Shk4OVskbUCpZnPHQHRZGpIQgpGFCrO5ajPRaXGSozhNjeH4ZGYxf30yXWVTm5+2sLfpqWrVTetNj978fQ3FNe0NQrIsOT4r2BUUDBka+Yk52ttD+DvD9THv+v6Lb71xmQcf3kw2U1kGwk58PkVvb4TBzS1EoyZawENYOrwxDs9sWW7c/FXKcSUfzkDUKzmXhqc3qxjV165Knt6sHgbHZiRDiRt9GEm+mFe78YDu8mGdDrk9obN3sAWvJhFCmaxPT6kkm8e+vQtL12jb0MpdsSCBqJ/OzhBFw2D8y2kAKjWHQrLEiy+cJxo18fm9XLq8QKFQYyFboztZIOqP0heS3JK4Oc53rqYeBtE6FaIBZi/lJHnXZLDT4KlndjE3W1B53e0hIh1hEuElFAGfxmk9ysHb4sRNBYo1wGgPkay5XJFBvtdqkmjz89OLLg8IgTWZ4ctT06usngA8popGfejodj54bW1enKIoSL5Iwu4lnd+FBdWJDQS9fPzFDPPJMhEv6NLlrVcucPS7u9nkk7z3nAoWMOpRMEEPSF1n+NJCPSFGNNkituWSy1YIBr3K47BkYVsOmq5M4qsVm+d+e5q/+pvbMXSNkuVy/KOrPPTIIAePbOW1ly9SKql2kRBqtL1jZyexqJ9PP59EAqVMhY8/uILHrxT7l10/w6MOXUEIeZRhegOcLs0Qf+etK+zb14c/4CEa8ykgh+KphkJe0qkyr7x4jtv29WF6dXI55a7w+JPbefH5c03B3ttvDnP/gxvJ5VSGeG9flLb2EB+8c4XhkRRPfnMn+/b3MTqSorU1SCjk4923rzAzlePp7+/hw/dH6OyKsE0Dy3apunDRNunb00c6U6BaquEPeCkULF547hxzswV6+6I8+dROnv7ebhYWimQyFXx+Lx5Do7sngq5BqWTjr4vLCvkKpukhHPZieHUK+Soer0Ei7qOQq1Kr2USjPqpVm1DYS3KhSLEicaRGPOGn8PqrhPZ9F7r33dC9UbAkn88ru7tGfTKnQOHQjeFbQHXqXh+XHOwX9NS7cXNlyVwZCjYMxtZeWxbKkhdGJbe3C+7sWPodyVRRdU4f6hXXnXrFzbpuwVU8VG/ET63OS9WFWq9qjlrjrYpNLO6jkKs070mJShoLBDzMTheYHM8Qi/sxfQYVW3L8xDRHHlPepw0qDqztWbohIrg0Cd/fIjg2L/nJRfirbYKZkuTUggL+IY9S2ZsatJrwYO/yNW06U2FkoYTtuIvELiEI+wwMXeDRNeJBDx/NKHDv8xpETZ2oV52D+bJyQkBCRxCqjsB1wZIupq6O7fREjmzJqrthS4Je7U8OmMLX4BQhBB6P5198HvxXLSkluYrNbK6mcoDrlyyo51c04GFHXQA1klMepoMRwY4ETRsSV8JwRo2NdyZYtgs2dcGPtsO7kwoovDspsVxJxNQIRNSYJOxRD7aKDXlbENAFtgvRmI+8rRaWbMmiPWIiEBRLLuMTWeYWSsTjATZ1RrEN8Ht0qpaDoWv4PBq6JhhPlSlWVcSo40oKFZuLsyUm0hXuGFDEzUYDbipTYaC1EQgt2NsqmSsLahI6rDJ//1/OIewaAa+BQHDg8BYee3wbr7x0gWqdiyYAn9/DkaNDSCSXLixw4OAWjjymfq5hVh8IehkbSXHvAxtJtPgxNMlARMNyFTewIQZwpSRdVYvwzRDhdU2NvK7kVAf72SsqTaVoqdfsCip19sWM4mvB2jGdiyVoC0jOpiRRr+CBbugL0/S4FUJDE9DVH6FnQwTHVWLCdFWycyjKJ7MxWkPQEVJ/x9vlZ9QUlIqykeRJJlOFTBUh1Gc/O6dG8W2bYtySuLn7ryEMsqXir3UGVffdduFqXvJoHwQCGtMiQF4L0r4Rjs84FCeUQChmCnI1Naq8tU0Q87pIqY7hg2nJpZRki9fhwQEllHvtqkvRgu6gxhepclNVv1LsFAx7GZkuUW5v5ZFv76KcKpDPLHJnPX6d1ydc8jUo2jAYVQk53RvjOLrB228NI6oWLpKYd3ET00iU8nq0ZYC38eBuDXlIpsoqitSjY9UcNCHIZCtomjLlT6fL+P0espkKju1iGBq27WLbLjPTOQ4c2soLz59pAr4HH97EX/z1rSQXikpV3hKgpTWApmt89sk4py+kaSQ5CgR22SLs19nRrka1syWl6D7Uv/yzM/06Rx/fzvO/PcPxj8bYe3svxaKFx6vRPxAjl60yOpLC6zU4cGgb+XyFd98exjB0Dh/dhtDgb/71HczNKmunlrYgnZ1hTn4+ydhYmocf3cJrr17gyy9mOPL4EM89d4YN/TH23dnH3FyR2dk8e2/tpvXAFj78YISLFxbYeUvnMkeBPS0STRgYrXHymSrTMwVSCyX6N8TZvaeLQNBLOllCNwSd3SFMrzLbNyImpXKNUtGiWqji9foJhjx4PUFsyyWVKjedIEKahlX/LKs1h3LZJhrzUatZlMsOmXSFzs4gbb4c5ewM6Vf+nyR++L/dUPf0w2nJ3laxLEGpZEm0m9gUTxclv7sqeXKjWDbtafcryzvLUbzHtSrogfu6BZuj6vcqjuRSBsYLanK0v0Msa3SsV/3h5eDOMsN85tPxWU5znB/0gF1zMAydSMTH6Ehq1esUCrUmJaRWdRCGTsECmS1z4ssZjj4+hBBQKlnE4r411f8VW9kQTpUE3+gU/GpY8tKoixDKXnF5MItal1fW8FwRpINGM+EYJOTLLg1rrplshaAjiekCYYG0oKUtSEvUSyUquJRVlo4dAUXzcQHXVW46oHimGgr87ugO0baOH/a/9PqzB6der5e+vr5/7sP4o1ShanN6okClvoJINLwenT19EUxDo1h1CJiLxuCP9GrYrmQsD6+MSXpCcEebIlYf6BfUXMk/XpBsirAsY/5CWjJZVCrNvhCE6sjLCof5PKgzn3eIm2q8Y7mSgqNhmjo9fTFOz+QAwWyuRkfEx/RMhWefPUOl5gACry4IB7wcfmyIaNjEXaFY39QWbP7blZKR+RKT6TKlmsqnr9bfu8DFu8LL1m8INoSh23D46U/PY9kujmsQrC/gv/9kgtvu6OHI40Nk0+UmAIlETNraA800o7feuMy992/k8GPbKOSX+DRGTDo6Q5g+vckzXQrsL2Qkp5Nqsd7dosIMbryUwv+2NtjVAsemVKck4oUvFiS3tQkCHmUrs9J6JZbw09oRJhhYvvj2BBe7Iytrccwul3FmTR1eHJHcklB2RY2fbfIp61nPQh0ySPCYBv6gSTJbY1vEz8A6Fkfrle0qQP/kRtHkc+Ut5bebqcIPtiqQmqrA78Yc/AZ8WY9KBPjtFcnGsGQkr4Qc/aHGwSlhxqWM5IfbPRhae/NvVh3F6bIcSbgetLCW2KlYqPHQgc1EEpLRvM6CN0rfYJzWkKQmJS8Mu5QsiJiq4+OvX5IvjUkik3nsSg2PEGuCiHymgneFOM6rqw6WZlnEYzFsS4mMHMfFZxrYlovHo+M4rhqLFxdJc0vz7Kcm8/j9Bt95ejfDl5NkMhWSyTKOI5mcyCoOrSbpH4jxi5+cpFZTKUdLAzq8HjUGBfAbGv1hkKHVD2gX6OgK8M2ndjI/VySdKpNoDRAL+EgulBBCsHVrG1LAzFSeq2MZ9tzaQyDg4YP3Rti2vR2r5uI1DbbtiKIJwfClBVrbQ9yyp4tkskRHR5jOwxG+PDXDzFSeSsnm2AdX2TAQ48FHNvPGaxeYnsqDEPj9Bj290UUjVpZsFDWBx6ORiPupVCyEANPU0XXBsQ/HOHBwC8n5MuGIg9c0SKdKBMNeevui2JYSA0opCYY8CAGxuLKuymWrDF9OkctV6egMNaOD87kKmi74+KMxfH4PR5/YSZQLhP/1f0QAHkOjhHJIsV25qvP47qTkjg6YKsLBJZsCWU+kaw9wwzWalwzFWJeG1BOC311VneHB6PKf8RuL1mUSyRvjKv3p0T5x07SCpWX6dZ58YvsqUZSmi2YYw6ZNrat+L5+rEgp58ZoGui6QjkvM1HGlZHK2TOXYGD/8q714/fq6PPe+qIeP/K2kanBiTHKwV8WS3t8j1gg8WU5XKNccLs7WNQwIAl7BvoEo+YpDtmw1gWy2ZFO1XSp17xwFVwWj80VG54vqHouY+DwaIcPAbyx/Hqab97eaTraE/vQ6po36swen/62W5bicnSpQsSUIxWvpjvnoiJoUbfCtY01kaILBqFpsLmYkL12VPNQNYa9aVDbHVCLH1tji75xNwWMbVluLmH6dJx7fzgsvnKNcddB1NX71enXuPTDEeM1gY1uASzMFkkWLZNbmt8+eoVpz0YTAa2h4NG2ZEtPjX5+fownBYHuQiN/gwnSBdKkhNNHwGBoblwDZRjW4sdJyiXsVHSFbU+Icd6HE228Mc/ixISzLQTcUqA5HfWQzFSIRP6ZpNHPT73tgE16zgs9vEAx56ewME4mbWGtwD0/MSxYqku8MKorCH1IeTfBAr0J+x2ckJxdASpfekGBKLrdeyVsqUaiKzg+e2t60XvmqNVdSVjGbY6s/+8NHh3jlxXMU8rWmcMj0Gxw+so2TX06DobOlP0zEJ9bp5q5dhibY37H8a2GPWBKpKHllTHXyb2lRXYaZktpAHe4XvHpVAVNQ4Ppnl9SD4MkB5eEY8cLKlN3tCcGlLCR8EjscwtY1jPrnWihUKRQUtcT0GnR1hfGYgr2merycSbm8MqbeW8xUUZGWCw90L4KgsiXZ1uZndJ3uuUTiDftoCSw/MIHAo0kKhRrhsInXZ1DJqEdbI+nJcVwCfh+hoLfpP9r8faEoN7GYD8eR/O7VC9yxr4/hiwsMX15QNmEoGstT392Fr86TfeXFcwSqNYoF1QUMhrwcfmwbui4Yv5K+pv8kQLXi4vHqfHlymr2391AtWwwPp0gk/JRKFu++PUylZPHwo5u5bV8v2XSZVKrMntt6iERM0qkS4YgPn2mQTpcIR314PBq2LUnOF4klAnzw7hVSqTJI1RHr7Aqx99ZuXnnxHLMzDX9bnae/t4czp6fZuKl1zfshnamgaRrRiEks5ie1UEQIweNPbqdUrOHze5QNoYRgyEsxX2NiPEsw6CXREmBmqoAQgkRLACEE2WyZUrHGho1xlX6VLLMwXySRCOD1aFy8MM/e23ro3xAjkykR2HIHw16HigPJcUnZUeNa04Aj/eoqaFwjV3KS9oAgYCylDkl+c1mpuW9pubG1pmxLFirw4DVEle1+wV9sVabw1yqB4PGBf5rJ5FoiyHgioIRk6RJTE3lu2dW1KghF0wRfnpzmyGNDHPtglGrFxjR1kIK2sMZ9B4coaxr6Gt3ORlXrneJ9HWoCVnPhni7Be1MSvy7ZGhOrXBBsV5Iq1BieK+I4iv8ZCxhsaVfXQsRvELlGHNdEWlHbilUHV4JluUwkG5QFQXfc3xzZ267k3FQerQ5s68ydP/Tx8s9Wf/bgtFqtMj09TVdX138zVlJSSs5MFijVvT33DcTwe3WGs5JTSQWMhuKSq3mVmhHxqq7ZxgjLEkW2xtT45lfDkqMbJB0BjZ6gGtc1ar4sCXkFCb+2CmA0FpKG6GSpNdArsxoDriQUVJegz2MwNZWnWh9HBrz6Ml5mY6zZtyl+XRDTFjYJmgaz2QoeXaMtYi7jsy6thnoY1FDFL2x0Q6fmalRqNoVkibOnZylbLla5Rj5bwevzsHtPJ+0dRnOcf/7cPOfPzdPdE6GzK8TQjg48Hm0VMM3VFICM+wQH+haBadVRUbB/2EoiuKtTcFury3BOcCEDvR6Hn//mHLrr4NUUGFGL7I0B/uvVeEE2R7tLq/HZP/2DPUxO5BgfSxOJKJXsmdMz1EoW3/vmELMLJa6cL1wTyNxMFSzJLy+rDvJfblMm5a6U7G4VnE2pKFqAb26EqZLg09nFa+mlsUXe6jtXLXb6MiS1GEOtHiwXWn2KxrK13cA8OsTzz5/Hthx0oa4dj1fn4YPbKOs6DZwsUALCoZi61kbzkK4qQd7SKlqS3v7l6v2lZWk63d1hol7wmjq1JaHjPh0cKTh7ZprHHt/Oz3/yhdpM1TefXq/OkceH+OT4WBOMNkpKxSMNhr1Eoj5Gr6QpFizuuW+Actkim6k01c2GR9TN+TXuunsDqVSZXH1KkGgJIF34yX8+0bSnAtVNXeo/qQFW1WVhrkBLa4C77x3gp//wBV6vRiDoJRD0sHtPN/v295FMlpiZKeD1GnR3RxBIvD5lgh+Jmly5nCSXqzAwEMfnV5vEc2fmiSf8dHWFOXB4KzNTedKZMolEgJYWP7G4n3vv38j8XJFES4DWtiAffzTGxk0tvPLiOZ7+wZ5l90OtVsPr1Tn5xRS7d3eTz1dpaQvR2hqgWrEpliysWoVY3Ec06qNWc+nqibBhY4JSqUapVCORCOAxdSXGEhCL+WltC+LxCNXVdhX1xfTqTE1laW0P4diSF587y733byRkwK6msHH99UEg6A6q9eXbS4IZzqSUkfs3EoLxAozkXEIeQW89qC9dVbztrqBKJspWJS+NSfZ3CK6jCVX+y/+VmnMFSzKelxQs6A1pyLYYLZ1xTF1gJ/McP6aibH//6cSyIBQhIBr1MTtX5PaAh0cObsa23GUj/IKuM1VUAHOtcqTk7asWu8w0tp3AxWC6pJKfHt8AZ1KSYzOSk0nJHe1qAmW7kt+PpLFsF4GL36Oxozt83YS/pdUb99EbV44O2bJNumRhOZLZbBVHSqbTJabSi8es1VfPmN9gU1tg2YTzT63+7MEp8C/ejPZGynYlqaKKwpjL18iUlQp/sD3YVAO2+MCWcFen6hjt71Dq94WyGuW/fFUtUF4NNCReXSn/DvbBW5NwR5tkIKLArYsyVh4wHbZaBb74dO1OyXoxbw/3SD6bg9eyGnd3hKhYLnOzKTy6pqxsHNVN8tR97wxtMVv9WiruK1mVDx3w6s1O6WRRMp1Si8bKaqiHl5YQAp8Bpar6Q5GYj+mzs1wdyyKlxOPReOTAZn7/yTg7bulcPc4Pq/hEf9CD5bq4SD6bVcchUMT+VFVtDlp8qlNruar7fK1M6xstr6GxPQE7hMqf90qHkqNi8LQ6kAp7bg7wr1XTRclsWXHL1qrGZz+4NcGGgZgS/cwXuf2OHmLxAO+9c4WL43lida7USiPtm61MVfLqVcldHYLtSzisH8/C+fTy91ewBN1B+NH2xU3LpYzyEf3WJsGzlyTessOIKzmVlpRseGaLaL6v/u4Ah57ajaewyCltaQ8zXNW5NOfySF0IUbIlmaq6rwSCgbDkag7OpiQ76sd4MSMZCAtCQW2Zeh/UQ7Gm6Xzr8SGcQonRbJWHH93Mm7+7hGWps+TVBF5T5/Z9/Zw+Pd0U5SkbNIHP7+HTj6+ya093s0tXt5xVdmFHt2GaBuWShddr1Dmnl0m0BAgEvMzNFbBqDt/9/m4qJZvnf3uG6ekCwbCXcNDL5csL3Hf/Rl549iw+v4FhaFSrDoYhkK5Um6Af7Mb2GFSSJY5/OMamzS3ohk4o5OHf/Pv9TE/lSDYsqHqj+HwG4bAyO0+lS1SqNm0dYVLJEmdOz7JxY5xde7qwbZdspsKVyymKxRrbhlppaw+Ry1X41c9OEY74ME2dyfEsmUyZQ0e2cfHiPEIIlQA1pZwIOjrCCAGzswX6Nsaa94MrJT6/xsDGBM8/e4aW1gB37O9nZjqPENDRGSQQ8OLx6Ni2xLIqzEzmiLcqYOD1Guia8pUtVWwcVxIKe5GupFiwEZqgJRFgZjpHsVCjsyvCSy+eY+xKmkDQS09f9KaeTwf6BP/pvGSpjWXZlhgavD+lNmkPdAuyNTX61zXFd8/W4NyE0gtoAg5vWM5X/eesmiM5Ni0puxAyoCsgOF23VQt7JDMl6Lddbt3Xx+8/Gee223vQNHjmB3soFKqUihZeU0fz6Hzx2QTlosUzf7Fn2Qg/DsSvYbklgFtbQZTUdXFHO7wxLinaSiS8u1VQdiRBj6QrqJ7HJ8YyTWAqgN19Yfyer9YIEEIQC3iIBdROYEt7gOlslWTBomypho7jSlpDPvoSPrzrNGP+lOprcPrfQBWrNifG8ypqsVFCZ1tniPbI4tY3Ziqe4srdd7tfEMWho5gnOVEmEvOTaA8z7eg8e8Ul7FVxeMdnJHFTpY+cTkKvU+bdZy9cM6mjUSutgUIewQM9kl9ehow02dGu4SnUOG+om9enq/z3qqNsNDQBZthPyWp0GNeud6ckf71t+fvrCsClDPxm2GUwqkZeeUvx9br8kvbOMJpHWzYGUqdQYJrqIdkApqA6ZB8fH+O223o58+UMt97ey8J8kUDAQzDkpaMzRKViYaFAyIWMZDBC02ZraWWrkpBH/FF2uI2usC7UyDvsAWuJ3QnA7EKZhUicvS0399qpiuTUvOK2Xq/b60iJ8Ai6+qP0bIhSKzt13qKDQCUreTVumL6xXn00o1TFKx+q3+hUimHbhctZBc7fnFAn4G+GFkfrPfVOUtQj6Q/DyDz0tsOeDkF8xX1juZJgSKe7I462ZNPVoSnXA1nnBQqod6vg7i4l2nqwV/LJrLo2DA0WyvBg79ojy2DUTyIR4Ni7V5ibVaPoeMLPN+4ZQGiCQr7SnEZk0mUuXUjyxWdThCIm/f0xtg2188KzZ6lWbWxH8sBDg6oLk60QCKjs+kKhyttvDhMKezl0dBu/e+UCuqaRy1TI5i2CPo2Dj22nanpIj6YUTcNxKWQrFHNVwhGTUtGiXLYwPBquK6lWLEquer+RqI+Jq1kiMT+vPH8WXReYZgfJ+SJW3cHBMDR6elT80cx0gS8+n+ThRweJtwSIJXykFkqUCjUMQ6N/Q5Sa5XD5YpJY3MfYaBrT52HTlhaCAQ//5e8/x+fTOXRERcDOzaoura5pfPbpOFfHsquunWxWqekzqTIbNsWWrVWlap54PMTho9uoVGwmrmaIt/jp6AjjMTTlfiAktaoKY/DF/biOC+hYlkOp5uC6quNcKFTxeDQiEZ+yHpvIYXg0NF1D2C4//8lJkgtFfAEPT6/hixykTJHV5tANoWWmqrwvP51TTglFSzKSg94QPD4gCNbjQn2Gcl9ZWrtvcOS/svKWJFmGiSLMlhQwXjoV+kMqW1UdyR1xwYYlI/OlNKKhOFjlAG+cmODOu/rJ56s4tkMobOLze7h4KUk2V8Wq1DC9yrvZY2rLOvzrlUQyXoCQR0V3J+tTQ58u8Gpy2RRiXztMFBVfPFexqNQcNFw2tvnpjfv+4OSnpSWEoDvmozt2U0KFP6n6Gpz+N1BTmSq2I3HRiAY8eHWNzqhJPOhFIhnNqV1ymx82RhRHsVHXi4P7621+Kq5kOAMBj+R348rvrtNwee+18zjW8l39SoDhuBJNW886SbAtriIvF0U0GgtFxa/RhfpPCGXRE2gJ8fyIy4E+QWwFALmaV36rmljNF9SE4MEe5dE3W1Iq6ZhXMlGE4QxEfRr3HxzitZfOKU9UR6ILiAa9HDiwgS8+m1zWWRQooYa9x2HHzk4mxrNoGvT2RzEMjddevsDOXZ1Me0K4qPCCj2Yk0yUXUxeUbEnCp0DyUJw/EJgqW5eVPnqwdlfYIwRhj6TigKFJvGE/vWuIVq5XsyWYLoOxrv3YGkcqJS6C6alc81rzG4qz5q13ev6Qbu5MSWVQr1WaUBYvO+rOBTsT8ME0/HpYWW+BEux9oxNen5A80qOR9AlaWjSMNVTFhiboCqzedLX5BVFTMp5Xvoh+QwHjS1nJsWkFUAWC/R2ST2YlJQse6F3MSF+cNMTIpeIY0ub5357Etd3msz6dKvPeO1eIx/088e0daF4NISXBmonX1MllJdl0hdOZGebnChw8spVKue42IUBoGrouiMZMXnj2LMlkkUjURypZolZ1+OGPbieVLJHJVIi1h+nuCFLIV6jNF7g6mqFatZeIqSSBgIdsRlFjHMdFoCHqthCuK0klS6SSJVxXkstVCQa9+HzK1/PN14c5dHQrra1B0ukS+XwN0zS4+94NpFIlXAnBoJdSyWJwSwvpdJnkQgmkSqAq5Gts2dqGJhRV5YvPJ5mbLRAOm3zw/ggPPbKZ2Zk88YSf994ZxlmD/w0QifiYmc4RTyyarjeqZtnE2z3ouoZlOeza04UQknLZJpVUFlJSGliWS6VqEwwITK+B7tEJhwxcqYz30+kyra1BhAapVIlAwNvsMicSAcoli523dBBP+Onrj3O2YuCrScL1jp7XLlL87Bd4b3+GmqGmQo1QlKmCEs/ETMFdHQpM5Sx1PZ5NSSq2umc3XVN8eG2CoiNVl1ICuap6vYKtTtamiKAnCLe1iVXn76tWyZZ8NKO6vesJNRul+XR27tvA716+gN+n4/d5mEuW2LqllaEd7ZSKVRJrpM1dr14YlQQNuJqHR7olJlC1JRMlFUgwWYRtsfoxCEF/CCqWo1T5gEcX9F8rCe7rWre+Bqf/DVRD6RcPetnVq7oPU0XJhVnJXEnZCm2JqQ7NK2OSiq14or1hwaDp8MpL57Bq64NMr19ne0KJQp4ddhFCILIFnCUPzKUppLWaw9R0nulgjMmCWhh3JBqZy8tr6W5dmZJv54UXz+FaTvOBHVuSrR4tuHyZgvtWEPVPJ1ValanDLy5L4qZctXsPeQShaOOcKZXz3Z0KaGgE+Jsf7mFqIkMmVyOeCNDTFaZatUnVH7yN0nWNJ765g1dfvsDEeJbungiJlgDHj11lajKH32/w6OGtRBNgO4IXR12e3ChwpNoYBL0ac0XJfFltHJaKy262LqQlJxZgMLKax7hMNb9046GpEb/h0fEnQlScm2fNdwfhyAbB+1OS50ddNkcEnUGuOwpcyvFVxyLIuZKqKzHrIP1G6Btr1S0J1aXsC93ITwt6g8reZmlti8NHMzBXknxVJu7DvUokEfcpoRbAlqgSrL07qcy7/YZgUwQuZdXGbaqo3mzCp8aFmlC2VSKfx6k5WJaDbavOnMejHDbS6TKTE1n6NsWpVhzefWe4yTmtVGykhIX5Eq+/epHHv7mDSxfn6egI1QGiyeRElm8+tZN0uszUVI5gwEsg4OG1l89zz32b2Lgxhsfr4e//90946NFBTp2cZuvWNvQVu798vkq07mCg6wqYSld5MTZEWdGoj3SyiGO7lIo1CsUq/oCHO7/Rz8//8SR7buvijn29GB6DVLJIOGLS2R2hkK/y0384QSZb5ft/uYdw2EtPb4RKxaZWsQGBbbv4fDoXTs/z5uvDCAH+gMHho0O8+vIF5mYLbN/Zgd+vqAsrebc+n0Eg6KFWtZVoSa4OVbYdl3DMxNvwKPXoeAwdLWjiSonX9GDoLn6/gc/vRQhJtepSs8C2HYJB5d3q9RoUcmV0Q1CrOTiOJLVQolqxCQS9IKC9PUwuV2Zfb2T5PZAaIfP+39O+cR+030LJlrw8pvLqH+kTS5wT1P9b65joh0PLXRXWq3MpSa4m2dumXDCKtuRyRokJXanoSBvDAhfll7q7taHk/8M6gmuFsBRtyUsjkrs6F4Hpel1jAUymHHTH4ZuPbSGbqVAs1mhpCeBGw5wv6RzZLtZV4a9XZ1MKmD7Sq5GrSV4ekZhlichJNEMFEIwXVPdUeceq4xxbKFGuKp/RbZ03tBh9XWvUnz049Xg8dHd34/H86VoupIoWKtd48WtREzYK2N++mCjR7odtccFcxiE7n6OQrDHl1ymUHGpSpW4IFIDwaOBir+pieXV4oFfj3GdlalV71QNTlWRirsyWvXHu6tBwbyLtp6Xdzw9+sFpA1djtDiUE80uwYrYq+WJBjYaPLOlA1a6zfTc0Zd+09G97/Tr9g1EGDW89OEDiD3l4+pndXL6UbHJKwxGTYNjDw49u5hc/PbksRcfvN3j6B3swDMUBupRRS2GLT8W5zk7lmagrmW/5JxAAdQQEQzHJSF4yWF0NDlemEKkSBP0ah48OkYgZfJWY5agpqJSVYvXhTsHVPHw8IznQd+3El7W6uQkflCy1yQoaYllk4PWq6kiGsxJTF5xOKW7nyhovSHpDyilI1wT5mjKuf3tScqB/+bEK1Aj0tUmdvxhMoOs3v0QGDcH93fDelOTeruUAdbYkGc5JugPwyZzkvi7BB9OyzquWfJlU3a+dCdUN/3RYWS2tjEmNRH0YhkYmXabXijE+mmF8LINH1/jhj25nZDhFJlMhHvcRjvj4+PgYt97Wy+uvXWJutsDQ9jYeemQzzz97hnx9NJ/PVbFslyOPDXHmzAwPP7KZ//j/+wSjHsGZXChh7jbw+Q0KhWoT4OVzCmj6fIpfKYTAcWTzmH0+g3DER7lsoesauVwV15Z8/vsJbr29l7/8m9tYmC9w4dwC7V0hNg22kE6XGB/LcuzYGNlclUNHt3H8wzG6u8PsuKWLbLpMKOxTyVZIJidyOA7sv6uPvv4Ybe0h3nnzcpMKceKzSQ4e2cZrL50HfGSzVaSU+HwGB49s4+L5We59YBMT41k2DSbQTQXAdd0gkahfBwImJrNEw35SyRKbNrewsFBitJ4St3FTHCGhXKqyMF+mWKwSDpu0tAYwvQbzcwUmJ3LKs7IlQCZdIhI1MX0642NZCoUa27d3cPbMrLKfCpqEY161NtlFcu/+GIDcuz8m8u3/mbdn/NzVIegNXRsc3ggwzVYln82rlLbXrkpKtiTsgU1Rwd2dgpB3venXjZSaGhTtRV/ixgZBCLVRDhhK41Crpz5pAu7pXgSma3WNQU3DkrMl3n75HAvzRYSUhKPKVeHOb2xAtyza/RrrNMyvWV8mJU8Nqr8f8Qq+t9WLbbeg6wYvjSmwfk+XYKKghJa3tal7tmwpz9LN7QFaQn+aHqP/EurPHpxqmobP96fL25jJVrEciUSjo84vlaiHfHDFp6sB6fky79SBSmtbkGLISzFbJhL1Ea4vyE49EzhXg6n5Mp0DcQyhiOntQUE5VQFYZksjhCAS8VETGhUb7m7x0+FXC5Am1stdXl0rBVSWI3Gl2wRw3QF4c1zSEYDeoOCNiXpu8wqLna/ipSeEoCINhFQE9ooNASHx+T2c+mIa3RBcPF8jl6vyzA924/cb/PBHt7MwVySZLJFoCdDWHsRxlLfkpYzLuTREvYLp6RLvvHpj/Nz1ynFlnbKw+N5ipmBvm2AwplS6Hk2JrAwBgzF1HlbyGGNxP2Y8TEHTrwtMXx93iZmCfWuIyTr8ynZsJKdMqPfewMNrrW6uhiDoUVxY4dGWRQZeqxrhAyGPijB8bECsSW94Y3zxtUxdskToTu+qcaHgQB/83TnQja8ezrEWQK25KoqwLyj4dE5yX7fgk1lFIbm1zt29JaFoJ5cy4LEdNF0sA6agOk25bIV4wk8o7OP9d67gupJazeHC+XnGx7Pc98BGgiGTZLKI17R56ru7WFgosn1HO/fcO0BLW5CLF+Y4+sR2ro5lSCVLRLb7iERNzp2Z4d4HNrGwUKRcseiMhcnnqji2y8cfXeX+Bzbx3rtXmsIqKeHkiUme+cEefvfqBdJptT5QdwI4eGQb6VQR07sIbD/8YJQjjw/xygvnqFRtBgbimH4P+VyFW+/oxbYluXyVXbu7CIe9nD0zy8JcUfFdXZidK9DdFaGrJ0KxUCOdKtHSGmT7znYymTK/e/UCI8PKjN3nM7jtjl4+PjbGA49sxnVccrkqoaCXSNSkWnW4ZXc3H74/wsDGBPG4j7bucFNQ5vF46+cdDF3DdhyCYR9nz87S0RFmcEsLtZpDJl3G5/cwPZVD1zW6uqMIDTKZCqZp0NUdpr0zzNRElnNn5mjrDGIYOulUidb2IK6jLIBOfD7JAw9u4s3fXeLIE0OKf50aoTL2BQDFkROY8yPkrR1YrgKWEfOrg0dXqpjTA32CjsAfQwQluLtL/X+tcqS6J02NVetbs1Z0jUGlUBVKDh+8dp70QrHuIao2S9lsldnZAocf305Xpwducg5Sc5UIcikFbum18MRGyXTdRq/NL0hVFFC2HJd82UIAYd+//Pz6f8n1Zw9Obdsmm80SjUYxjD+90zGbqwKqW9HYpb03CQsVl1wNBsLKjFcCu0KS3714rskTLZctenujyx52Qgh0BH5diZLicT9vjrv0hJSVTrfu8NJzZ9l/V79S+NbHh6BEFvGEn1hEZ6B3KcBQvKiFstpp3khJKZkoKPV1qx/SVZfb2gQJU5mvvzMp0TqVjc7NbE5tV5m1dwUFc2U11vUb6jjHcg7VcomKHiDs1Ymb4PNIBmplHv/mdn72j19QLqvo13feusLDBwYpFm2EJom3BEBKUqkSXV1hTuc1Josujw8InIrLr352YZVF0I0KgObLKg1JoGxfOgIKgPYGaXrLhj3Kmmq2JMnUVDLPb4cl93Yrm7CVjglvj7t0h64/zi9Y8HDP+t9f6Td6I7VWN1cgaAkpv8GXZzSqjosjYSgm1qSD2K5Kd7qjXawyAF9ZB/sFv6tbSFUddf5mSyyLdlxegn2tDsdGi9zdH8IwVn82x2cld7Rdu0u8DKB2wsmkZHNEcCGjNlSvXlX35dao4Ni0xBCCZFWSr8HOFo3LV7OYfhOP16BSsZdxqdUoVG38ZmcLbNnSim2rsfLcbIFf/+JLIhGVUV8qWWiaoLUtwPadHRSLVU6fmql7QyqbpYDfQ7VmoxsaO3d14TUNLl1cAKl8XMNhE13XmJst8P57I9z/0CDSlcpvNGzS1RPBshzuf3gz6VSZXKZMNOrDaxqc+GyCO/b18eEHo01gOz9X4JOPxnjq6d3MzxdIpUqEwz7a2oKkUyU8Ho1SscrsjAo2kPU1ynFVmMCpk9NMjGXI5apEIibtHSGuXs0wMuLjzrs2sH1HOxs3tihHAr+H48fGqFVtsu8M8+DDm8mkysxO5yhVVARqPqu8YXfv6WJhoURXTxhLgus6FIslgsEAmqZCBt549SI7d3ViGDq5TJlA0KRac3ActZmIRH1cujjP6JUUCMHcXIH77t/Ih++P4DF07r5vAE0TTM/kSc0rv9aTJ8bZf1c/b795mWDQgy/oYT5VYnI6T1e3l8LbP6Zsq6aBJkB/98c88NjfMlwJciUn616jkqIFj/QKouaNbdA/mpFMFZXgaKVAqlE1V/Hae4OCsi25nFUOE5LGNEKFi3xVQZUuFP9+vVrZNXYf+1t+PhYg6BHcqRUol2rLNnANSkm1bFHMlvF5NRx/BI+p3fCU6nRSPTuX1sproWvJ+Ur4BAmf2qg1VtWJdIWo/093IvvPXX96aOyfuBzHIZvNEgqF/iTBaYNv2h4xm12eB3pUF8ZFUqwpD8SoV3BlOEO65CCl8jYtFmqEwmbTsNiyVMpJg3Bleg0GesNsMuHzefg0BwOVApmyze9PTvPokW28/vKFJdnzSvz00OHty8bVk0WVhnS4/8YXLxfJu1OSh3qUsXHZllxIK7X1cFZyR7tSXPeH1o7Bq9R5BD599dj21auSW1slp1NqIXcROC4MRST5TJmWlgBG3YqjaMPsmMP8VJZ//e/3MzOl7JBaWgN0doRJpcoszNeo1hz8Pp0NGxN8WjBIV1yObhDomsbUTHZN70q4vgDoVFJyekGldT3Qo45prqzsiZ4blcS8km1xwUBYeXB2BBYfMre0qASXQkJxUg1t0cfTQY3Crx1pqri5Duv3HW6kWzOSk2yMNM7+2qr0pfSNXikp24JfXJYE1ljbJZJXrkr2tAj6w9f/+z1BwY+2K16nLdVRzJYk0WtsaobiktO5Em9P+LinVyOw4hrbEIY3JiQP9dBMqVqrGgD17XFJ1FTn4p4uwZv1uN9HehvvYTlfUNcEH42WuTyS5NBRdZ9VVkToPnJwK1euJJv3sdA0DEPRa6RU4qNcvkqgfhJff/USR5/YTqlosffWbvKFCrPTBcIRk7bOELWqw0cfjjJ8OckT39xOS0ugDgqrmL7FrufcbIFf//wUsbiP1rYgpmnQ0hbAMHQ++mCEktRpDepMTWRZSJY4cHgrJ09OMT+3CGyRkmymQipVpKc3SqI1QCZXI1l2aI/6eOvNy6RTZdW5EmDVHFwpyeeqtHeE0HWNsqM2irlctTnF8Qc83L6vl0jET2engaZpJJMl9tbN+1taA7z71mUuXUzWBWIC13Gbfq9e06BatlmYLdPS5qPiupRKRfx+H5qm4wt6uPWOXo4fG2VoeweuoVGplvD5DQIBL1bNIRA0aWsNkc6UaWkJsmdvN599epVTX8xg2y6lssXDB7bQYrnMTOepVm3ufWATxz4YIZUqc+Tx7bz9wShlG2bny/SFk8wNn8CjqahOQxMw8QVt5VGi7bewdIOZrCg7NNtVnsr9YbirHYLe5ZMlibLyy9Uk3xlcy3ZIpbBNFdVrzpfhpK7oWVuigqMbBH5DNF0p/qhV7xrXHEn24gla7xrlyY07aQ9ofPFpGbsuytJQ3OClhOFksowv4OWT4+MMbW+/4SnVSE5NPJaeW3fFtbBWCSHwe3WqNbncPefruun600NjX1ezXCkp1RwkGokl7UPbVf6TPl3x+QR1n71smYhHUHEk2ZryiDtzeobDR7fx6ssXcGwJdbWzNDQOPratCTL3dwpmyoLRU2Wl8E+WOXFqhgNHh6gUVbZ4KOKjry9Ka1cAt75CWK7k97OSwdjNxdZ9Mgs7EouJG35DsLdNfe/WNjg5r0bY9y/p6rlIrmRVV2w0L7m7U9RB0WLpmvKanC7BdzeLZeDVtlcfX9CAvu4wx9+9wofvjfF//r/ew4aBGK+8dIFf/+JLACIRk0SLn4OHtxEKG9wXllzIKLHQ/s7lAqC16loCoN0tKqlrT+visTWyrbfGFH/y2IxkoqCM3Ft9yly7P6zU548PwBcLSiQWM9UoOewR3NauUcjbXL2SZXq+TDjmZ7AvjG8FBzbshd8Mq8ja/R1f7SE0XVRK+jvaJV5da4LhtfxvXVQ30NQbKuDVrzeWU5/LjQDTpdW9ZIQf8kjOpiR3ddYV7Gs8YLtCgja/4O0Jya1ty3+/KyAwO+CdScmDNwBQbam6W/fWgakh4K+3rW8h5krobvNz7kSF06emeeTgFkrFRS/dYMiL47jNpKMzp2c4dHQb7751iUTCTypVXuRUHtrKhfOz3HlXPy8+e4ZCsUYopMQ99z24CSmV4XshV1V2ahJcB7p7wvh8Hspla81xfiFfo1K2eeyJ7QSCXqIxk6e/v4fRiTyFTBmvIejuifLqS+eYnsoTifqYnyvw0nNnefKpW8jkKkzP5AhHfRSCIcbsALe1QsBxmE7V0FzFfQeaAMT0Gdi2y5HHh3jp+bOUilbznPl8BgcObSWTKvPCs+dobQ0QCHnxenXKJYu52QKRqI+DR7YxNpqlVrOXAdODR7bVebA9/Ke/+z3/6t/uJxpfvjvSNEgtFNi1uxspJelMhcHNCcJhH/PzBeZmCgRDVTZsjDMgEiQXimi6YMtQO/FEkFDYS3t7GMd2CYa8bN/RzvxcgdErKXbe0klrW5ATn09SzVYIewS9bSbOJ/9rU7G/tBrc06UczBaf4Jv1ydKmiOJxfjwHVdel3ScwNKU9kCjv379aYrtXqFtCzZQlsyXFs2z3w64WZUFYsZeHtMC1N6dzZclCWXVWS5YCwkIoYapRtwkE5aXdtgYdB8CqFEi9+WPyNYnrKi2FfvzHdH37f8aSIeItAeWmIZVTxEolWzTqw7EdunqiN2VTl6uxKvHwWqVAOtiOpFJTSVANf/Gv66vV1+D0T7gyJVsRvYVoRpiBSgQpWspwf7oo2dsGQ3HRFKL4dJVeka1BbrpErjbDwaND6BqU66kZgZYQVyo6baCEPDN55jI1WiNeTE3tEGu5Csfev4LmU9nizObpaA+oxJz6IvHSqCTmhVsSNwck9rau7no2KuxRHKb/dH7prl3y5rgkZgoGIsrbcj1fuYRPsKuFG/ad8/mVZ+JLL5zjH/7+BM/8YDcHDm1l+852kguLxuGmqdXXRsG2GHT44di0y7a4n5ylFK8ebdEey1u32LqeAOjbmwTrYZ+wV3Cof/EcTBThTFLywQw8sUF9/452uKNdMFuWTaAlcmVefuE8lqW8RgMe+KLuAbi0u3C4X1Bz4bkr8iuD0zs74M0JKBRdigvZVdGWa4FyQ1NpN+fSLBOugerCb77OKP969e1Ngs/n1TUE0OKTPLlxtfK4IyA4EBL8ZliyM6Ee1I1KmIJ9NwBQP51zsd3lwPRQ37W9bRvcXI9HY2wkzehwinDUJBjwcuniPLbt8r2/2Es+V0XTBMlUiU9PzvDIga1YNZtaVfk8hsMmlu3w0CNbKJVq7Lils0m/8XoN3n7jMrfe3sPMVF49yF2J19RpbQty+eI8z/xgDz//6cll43zqPqnBoJeWVhUdGY6ZOFLi8etMB6Ns64vjL1b48P0R9t+1gUy6TLFYo7snQktrkLHRFLF4gJ4NcbSQn1LN5d5OxfHTULSPT94833QRMQyB6VPRt8c/GuPhRzbz1NO7uXI5SaaeYuUPqPE9wNEnhjh+bIyL5+eba5HinvZw5fICP/zR7VwdS5NcKBGJqN/9/PcT3HZHL8ePjVEuWVw8P8fOXZ0E/YuaBMcFX8DLb355mg0DMfbf2c/UZI5yJUV/f4zb9vXgupJCvkYqWSSTrhCOOnR2hojHVcQqUjA9nSebrRKP+2ltDRCP+6lUbF5/7VIzCtfr1eloERTOvbvmNVIZ+4JIaqTJwWxUtirJVCUP1idoOxPqa+mqmgRdLSifYo+m0v9aTBVV2hsS1BzJYFRd19qKe+FmA0IqjtrcOi7EQtCH+nyrrgK6pq6mKJ/PS0xNKfN9huBKTllIATxujlAZPUHIWLTca7xv2X4LHR0hgvVY3pXLiM9nEAp7cV1JMlnC49XXnFLlapLIEvCfrKw9dVuvyrbk1THJwQ2CpXuItWKrv64br6/B6Z9ouVIysqAcgQNefRnQWtplc6VKyHl/yuXOzjAer0al6hDyLOkYFsocPz7Go0/tYaai4/FBd1iSy8HMTIk3X1F8SVtK7r1/E6WihelTSTC6EFC1qFQttZguEbO8PKbENGqRXF7XGydfb2HQBM3YvJGcy7FpZczs1eBiGt6ZkGyJrd3tW3p+bqQqZYfTp2Z44ls7SSVLXLmSprMzTEdHmN6+KLWqw8S4Mukf3NyCVheWxUxBb0gi3RDRgI60XKqupFBPavKarDpna9XaalvJ5/OwJbaoBlf2SIoblqlKfjsiGYrBvg4Fgjv8SuyTLzj88vnzeKWDV1cdDA2xDgdWnVOQlG25qnOyVl2pj/EbGwePJtiilXj5V+exl1iWXU8Qtq9D8NwVyc74ci/YiFcJEAav6dl47TI0wf4OZeP12yuKp/t35yTf3bz0fNaPUxN8c6MKeNi1gld3PYAqkcyXVAb6jQLTRvkCBo8e3MIvfnqKStUml6mSTpXxmQaHjm6jkK/gSol0JI7QKGfKfPjeCMGQl0jEZO/t3RimhnRhfqbIW28omyXLcrkynETXBXffM8CxD8eIRJWa3vQZHDq8jWrV5tNPJnjyWxH+1b/dx+R4lmSyhGO7dHSGSWdKnDs7jyYkR5/cjrPiYV+suXS1mDx6aAtzM3n8QQ+xhB+PofP7T8epVG1SZZf7HthEVWhN/1lQ6WlnHD9Pf3/RtSMa9WPbLl+emiadLnP8ozFuu72X1tYgFy/Mc/H8HLlcFSGgvSNES2uARw5soVisMX41QzjcAK9X8Xo1tu1sZ+OmFgIBD3N1T8q9t/Vw/NhYU+GfTleYnyvQ2RWG+hXqui49vVF8psHolTRXLqfo7YviOC6ffnSVSNTHE9/ajpQCn99LXLXlqVYcEOD1GGRzVRCibi/lwec3eO+dEU58NklLawApVJTpoaNbcD/+f1/zGlmrezqck3QEYelGK2oqHupiieb1+cKIZEsU9t5AoMbNVP8qF4G1X7urHz6elfzmisSjKXHUkQ2CLqNE7rf/AelZ/XuN9635gxx9YjvP/eYMc7OFZQ4Rhx/bhukzePutKwwMJPD7PWtOqT6ekURMyZ0dat0W4uaA+GheAfugIajZsk69EcznLbJl62ve6VesP3twqus6kUgEXf/TasGXag75ihrpR9ci5tVLE8pY/KMZyUdpjfsPDfHsc+cJ4DRBTyM1oyWmE6qp0fizVyQHOiT/8A/nMes/qwn45ISKR/zdKxfRg4tq5gbQMP06lpR8MKU6hCuBqQZU653YlR20m9lnNjqNY3nJuRRsT6h/70gIdrfCg0tspW60NE0j4A+gaYscLCEEszN5CqUaJ09Mcf7cHG1tQcaupJCoTnOxaDE7k2ffnX3E4n7aexbB5va4QMMg8vh2Xn3pPNQcTJ8ymK5pOgcOb/uKdlKCroDqhHYEYHO04TmoKmYKfrhN0SNeGJU8MaAWzIBHI5nOKm6cqc6j7SpKRAOgrtVd6AgIpktrj9lXVqYKZ1PK6B7U5/27l87jxVnWjbmeICxhCuKmEtMtBS/72uGVq8pCbO8NbzQkH0wrX9ulG7mYqfioIPl4VvL6VQWsox6NFr+/eS3omqInrFXXAqjpqhLsvTnFdYHpyg2bJhTl45GDW6hULFLJcrND+MnxqwAcOLSVV148h+5RIQOg+Jn77uxH6GDVfUZb2v089cwuLl9coFy2iUZ9JFoC6Drce/9GpqdyRKPKmika8/HS8+fIZau8+vIFHnhoEH/AQ360wuXLC8xO59F0jZa2IIee2LHq+u0MCE4nVTiDx6/TPRBjQBdUyg7TUzm6usLE4n6ibWHemte4fYX5e9mud/aW0D6QsDBbolio4Uq4OpqlXLJ4+NEtHH1iOzPTefL5Kq1tQdralGPGL356kq6eCPvv6qeQqzA3m+fBhwfpahX49VkmMnGOfXgVpKRUrC1zHwE1Ep6bKxIIemntDDTXnGDIw/f+Yg8//8lJisUa83MF/AEvluWy784+3nt3hE2bWvAYgkDAS81ymZnJ09Ud5rVXLrJxsIVTJ6YIRbykkmVs2+Ub92xg02CCbK6KL+onkAgRdUfJn3xZoaU1rmcQa3ZPJ4pw3w0KTwXKUu9cWrL3hn7jj1GC/e0KEH42J+v3o4C5RYeCldV43277LbS0B/jOM7uYnSmQTBYJh0xa2oIUC1Xee/sKc3NF9t7aTXKhsOaU6pFe+M0V2BxVlm6nk3JNseRaz4eZkhKrbomqJtCdHYLt3WFGF0pUaxYL+a/B6VetP3twahgGra2t/9yHcdN1ZYnZZ+eSLXHZlnw6B/d1L+cDfaNT+W4uVAJ885ndpOYKuKUybUvAoRSCuE9wqwmDMcnVKxl06ZCxwKMpyxs3XebsmVkOHd2GEFCq0wAarzFeVBzI21oFg9HlRPvrpVE1Omg1V1K1WZNn1aiiBT1B+HBG8t1BxWe9re0PO6eaphOOLEdfDdP4csmiuzuibEoylWU/Ewh4mirdudkC3f1RLHvx/a0nAIq1hfEGbgyYnk9L2gPLfUy7gopTOlWEkwvq89mZgEhdqStQ6UTn0koYdahfAbN0qoxXX4yFlUCqonw2JZBa1V2QjOUl93Te2AOvPwTnM+rfDXBfsxw8BquaJ9cThKWrsGEFIDY0wRMDAuumomgE00WXD6fhzs61lMyqk/p6VUWKJqsaH2TC7G0V7GpRD62+kFLp729fTQlZD6B+Ma/Eaz5jfWC63obN0QSVqsNbr1+ipTWIKyUXLygQJVCG99WyxaGjQ1i2S6lUo2WdzZ6sq/o/PTlLPGBw+cI8kZiPbUNtVCsWwaCXeEuAzs4QCwtFMpkKtu0yM13g7TeHefDhTey9rYfkQolyuUa8NYSvJcxwVaNzxfvpDSl7sUZ9PCcJeSS3JLQm2MzVJBfTLrmqJF8TEFTAZLqkxDzPbKl39pZ4JLe0+/nO93czMZ5leiJHR1eI8atZRoYXSLQE8Pk8TEykaWsPYtsO/+rf7Vd0pJk87R0hNm6KIcoLlJ/7n9B6h2i/9/+CdFxm653SpdUw5td1jZmZPG3tQRDqQGwpaesI8aN/pzrKqWSJ9s4Q3d1RkskirS1BvF6d7t4IczN5KmWb3t4Y4aiHW2/v4a06laJUtPD5vMTjPnp6okSiXt566wo7trTjuGUyr/0vVB219jZuDQnUHOrcTfW1ld3TQ33imhzoldUXgrcnFec0tEaX8o9TKhZ0OKsCLzwatAWoc2DFMoX+erX0fZs+gzNfzhAMe5mezDPz3gi5XBVN1H12wyZWzVlzSqVpGlvjkvNpZfV2T6dY89mz+vkg+XBaMhBSjYGwB0DQFjYZmS8BgvlCjcH2dWwQvq5r1p89OHVdF8uy8Hg8y3ZE/5LLclxSRQuJRn9rgJC5+DH6DeWLeDmjRr6NqjmSog0dAYnr1ynrURaqMb6sShxH0L3y4dgdJZcuU7FVB9Sqq7Y1oaL3PnhvhG/c08+t+7uxHYmUkoojeW9K8viG1Td3uioRNXcVMIXVHbSZIlzJwYPXsDDKW2qR7g3enNDqWiWlxHFsdN1Y9LeUEI74mJnOs2dvt7LPKlvLfk/XNTxAKOhFaCDEatBUcyUjVcGmDTE2DMbXpTOsV14NTsyr0XrMVOlC3UEFknpDaqx0taBEPjkLBJKBiOLfbo8LyrZyN3i0TxJP+NGFAqd+A6Je1dVzpPp8E/XugivVuHu+pLqJNxqzGjeVGh4EtlTiOU0okL7WfGI9Qdh0PTVpLTcGWO5BeP2SFCzl9pDwLXZ1l5ZAcLB/ERTtSdicyeicmNe4rU3wSB9czsD7U3Bf9+pUm5UAVRdKCBYzrw1M19qwxeN+dt/araI+/YZKvSnVlrwb8Jo6mq7xxpvD3HFXP/vu7Gneiys3PDkLRmfKOOUaV6ayuFIyOZXj3Nk5FSoRUFHHfr/Bp8fHefTgFl57+QLVqrJa+sVPT9HWFuSpZ3aRSPip2i7DJcEt8dXnsTOgLMgaSu7b2uAnFxQA2RZT19mltMR2BY9vlMRN5UpxMaOmAKprtfpc1VwFXj//fAq/V6dYrLBxUytt7WGy2Qoej86O7Z28/cZlHnp4EM3QCIW9+EwDr2Hjzpym/MH/h/Cm28lv/xs+fOE8j39rBz/5zyeW3dMNcdSJzyY4fHRIxa+myrS0+rHr96xhalSTNsGgl9GRFAvJIgJo7wzR2trD/FyBK5eStHeF6WgPkZxNceqLMntu6+bI40PMzymh1NZtrUSiPoQuFX82XaaWyrNtg4fSnU/h7n8Ky1XddA3q9mHKeaNuZKDoAppL4+q4GWAKShcQ8cKxaZVq1uFXNnXaP9G6WrYlH89Iqq6izJQdtWHrDgq2xuD+7jX0AanlXVO37mG61Hd4adfY9Ctx36svnadUspoddp/P4MDhbXx5bq452VurGeC4kKyoydB6TZGVz4eTCyq2daXVnZSyzjeVVCzJ8SsZgl6dgVY/Yd+fPeS64fqzP1OWZTE5OUlPTw+muU4w97+w+nIiD6gHVN8aub13daqUjxb/Yqctb8HFjPLMlFLlmW+OSu7q1CjMl/iPLyoOoi2VsrK1xc89+7qJrrhRc5baXYY9kkDQi+1Iqo7LcFZ1Pu7vXg1MXSl5axJuJ39Dlko9Icnv52QdPK29UMyUJFcL8PTmGzljDdRz7cXWcWySySQtLS0YhmpLlIo20nXxejXOnJ7m8GNDvPzCOaVqhmZU4l13D3D+7Az3P7yZs0n4fM6lxaeAVbamMtSrLpxNuQQ9ahy9nkJ1rdoUFWyqR2CO5tRn+eG0pC+sYjtDHkF/SBDxwAfTqgN6MS35ZBbipvIDNTTBc1dcDneGCfl1SnlnGbdXF8s5sGdScGJBWTat52fquJL5ihrlNl9HE3jranvLkYiQH0Oo6yriXW09s9aozZGS1yfkmnzl9UtScdbmK0/VR/I/3Cau6U3a/PuOTTqVYnsswcsTgp6QCh3YElOdrBsBqP0hCOjXHuVXy86aGzbd0Lg6miadKnHg0DbeeesyxWIdftS7QYeObOPUySmqJYuwT6dStPE0RXmLdTUvlS9rh58LtrPK1D+fq5LPVdl7WzdzM3nmZgukUmUOHtlKqWg1HQL8AQ8Xz8/T0x8lGPCwpS2IvuKvzZYlJ+fV18o2BAzVpf7BVvjJRUnMVOcxU5PckhDE6/zsz+ZcHuqh7s6xmrdbsuHjGeguFShmKxSkCh04eWKGUNhLIOjl4oUaUsLUZI5f/uwkd969gQ/eGyHg1/B5dR7YFyKw8Xacvf+Gf/zxp5QrNm2dQf7Vv9vPxfNzpNOL7/PEZxM89MhmPv5ojPse3MToSEr5j/p0ymWHiZkMbW1BKhWLfXf2MzOdY36uiBAa/oDB1FSWXbu6SYSqpC+d4IsPUmy96w5OfD7Fbbf3Eg57MU0DXReYps7pU9OARiFfJZuuUB2Mw+ADaDQNVJr1+rirRKfAYFQwEBas7v3eWF3NS/weuCUCZ9N1D+Ci5Hxa2c3d2qoU+zcqHl1ZuZrknUnJvnYVb5ypqvvzWtz1tbqmVUf9F/XKZQB1afe0MZ2ans4zv1AmFvESjfpIVVweO7J1lRPJ0vLoSqEfuca0bunzYapscDYt+cGW1T8vhGBbV5iLMwUcx6VsQcWyyJQt7tuyxq7461qz/uzB6Z9azWSr5Opc08H24JqLhk8XPNLbyOiWbI0JNoTVaB+oR3PCRAEqRYcXXziP5jrkHbU7j5rgFCogwePRsKzFWzriUSrMIjqtHQrEfDKrdsfPbBZ4lwADicRxlZDkjnad3IUbs1TShQIDVwuq07JWzRaVBUmqCt2GrD/OVp+LY9PKzH8oIVapvq9XQihV7bHPpjn6+A7efvMyZ05P89gTQ2SzVfL5Cn396gDPfDnN7r09+AMegjo8s1k9dPM1iS0FcXPx+AqWbFrk3Ew1MqgHIjAQUdyyD6aU/2ubX1EhLFdlancHBWGP5GRSdV0+nZVsi0NfSPBeUnDksSF++dvzlB0Hn666hkt5wyVHMl6Q3N+1aOe19jlSlALZquyVGtWwVfTpsKU/zDFdx8ShYFEff9V/bg1BWEOkEfaoDtHlrKIcbAhf27qmYMG7kxJDU6PhkAGmoTY4m+viqYWK6uzdaBm64MgGwXtTkn3tyulBnY/rA9Q3xyW7Eut3nJuUhzU2bOWyRU9vlDOnZ8nnq3z7u7uakaSxmAJQX3w+ycR4FsOrIyX8/CcnOfr4EC3tARwpWSgrik2yoryP+71hNH3tC8/0GQRDavTpOJJUqsALz54lEjEV8Ksnoz1yYAvzs0V8/VFkxeFCRbAttvj+OvyCmZKk088yb1hTF9zeDh9NS761STCSU/dvI//9lhbB5/NyxbWmghaiXkVNubNTx5otgwDbcnFsVwWINHxO5WIaU7FYo1KxQbrMTGQRQvBqscQzf/VvOX16nnJFdUrff3uUPXu72bmrk/m5AnNzRQxD49DRIT75aIy9t/WQy1XQdQ2r5mJbkt/+6ktqNYeBjXG2DbXz3tvD7LuzD59pMDWZxe/3sGNnF+GoB3fyBLWX/gfufPj/wadnJti4rYdzZ+Yw/TpdnWGCQS+vvXyB0ZE0Bw5vpVy0iMf9qyyRQAnFPp1VyvJvdIp1s+ZvtC5kJCfmJd/eJHh5VHJ0QNEBGkKmZEVyNq3W0P4w7Kmz31IVWU+Suj5gLdlqwtVVt2GL30D/x6u5hO54itDtTzW/JlEK/6ojCXpEM/5U19TPV6TaJOcsHX8iTK/PoJivggAzEqCq6/g0DY+muqSuq55pDZ53Z0B5dN+oPd2nc5KHe9Y/B60hLy2DcUo1h7FkmWS+iuOqiedSZ52va/36Gpz+idVMViVCeQyNnvj6C1PII/jWJkHBUiP+164qU+FWv6iPR2BDRDB+JYsHh4Ktdo9BYxEAfFn3V3zrjcvUmpmPgohf55Gj23hzXsMQLpkqHOhbDkwBXh1T6u6ipR5E42t0eZfW0g7aVFFeM3HE0BTwuJpXwMvQ1C69xacekBJYqCjl52MDguBX4FJpAvKZMrV8mYVkkW8+tZNkskQuW6GzO8S2WBszk1kQGrfe3ksk6qNSqjHQY1KtN7nWGhF9VV7X6aTkZFLSYqrPr+KoEdyORCN2UHVM58qqQ3Elp5JX7uyACxn1MAoYkp6QYNQJ8KO/3sPw1TzzqTJ97X56uxVXsWBLfn5J8s2NgoTv2seqCbURenNC4m1XoHSmpJbski0JGIJgQOeJJ4b43cvnKZecpvhqKRhe2tF4ZUyyISyI4pAcL5DPKB9WpzVEDp29bWuD1JBH8NhAfVJQk3yRVMk2BUs5G8DyDu+Nlt8QPNhDvQN04wC1KyipXYNQ3OAzr1WFfLXZWUsmS7z43FnuuX+jUqZfmKNUtAiGvOi6xsFDWzn2/ii1ms1vf32abz+9i6ruYaSqs68DuoNaPT5Y5/Ent/PLn51qdv6BpkXTqS+mePDhQfx+g2xWfW+pwX2Dh+k1dSoVi9KURUd/Alb0o25tg3yNVTVfVnZGoADRu1OKNrK3VdmdnU6q7zUe+KeS6pVVp0xZeY1n1BriOhJnDQ+yaNTHxfM1HMcln6sS8OnkUKPWchUWkhWy2UXOuETy4vPnOPrEkIr19amErYX5Ind+YwPVqsXbbwxz4PBWZqbzmD6DaMzH/FyR0ZE0AA88NMj8fIFy2WbjYILW1iC+gI6nlib16v8La/o85lv/A498939lquohl6viMUzS6QovPHeOXLbCwSPbOH5sDF/AINESYCFdZQ4vuapE1yRnU+qsbE/AbW1i3az5G63RnORyRvKdQYFHEwghV9EBWnyC++pG9JcyLu9NqW6440LZkTzUq9baa1XIsybOvmYVtDBsfGDV1z3AdN5luCoo1CQ+Q3XgnQmouJINIY0Oq8ybL5+jVKph2xJNF/gDXh57fIjxisXURJaW1iA9fVFMU2d8PEM6VUYG/RzpDqPRuJqXi/SClMktgUumrqYx66VqlWzJyQX4RqfBts4QHxVqIAWzuerX4PQG62tw+idUpZpDpmwj0eiO+VZ9vzGuW/qgDHmUef2GiIpR/ObGxe8p/qjilYY8qzl86VSZQr7K09/fzcz06iSfI1KSrgouZyQnFiQPdi/vEnk1lX/8rU1izUz1pbW8g6Y4SeY6BgqOq/w8/7K3wTdd/JuXs6prFvSoLrAQrAFMFYh7YUTlsa9t1aQ6jvGEHynh1NkFtmxp5csvprn7vgH8fg+5XBnd0PF6dUJhE5/PoFp1sOq8v2RF0Sk8mhKJ3KxNy3hBkfTjpqDVp3ilMVOJc1r8Yg3OpYJsHX7137YYnEmpxJgDfRp7W1VX660JSV9IMmlp7BiKU7DivD7u0mtC3pa8WR+nXw+YNsrQBPd2q67lra2CmTLsbBH8fg7u71aLfUdXgEe+vZtqqsB8skx/x2rhznxZ8vm8JOARtNbKPP/CeXw4GELl0ltC59BjQ3w04+eujtWAcGmFvWKZYvnEvMsXC+p87LxJz11Qnb+bBai9QbGmh2ujGtfXqhJKcf/px1d57MkdPPeb08zPF/ngvRH27+9T/Egh8Hg0NF3j4+NXmZnOowklyhu5nOTChXkefniQhBZgdrLA7HSBeMJPZ1eYx57YTi5baY7rQ2GTM6dn6klTXp741k7+y99/TqWyNg/z29/dxdWxDIVCle23tFMoLUeiQ3HBP1yQ9Tz1ZW8Lr6Ye+zFT8GA3/OaKxG8sTkiqzuJ9P1GQdAYEfSHl/rB0DbFqDvoKMNUAz7lclXDEJBIxKRYqy45gbrZIS2IpopCMDKfJ56qYppd0soRu6CAl09N5vvh8kjv29VIsVJmZLtDdE6GjM8x83X5qdDTN6Gia1tYA37hngGKxxuxsno7OMH4P+Lc/jH/7wwB4RYpwuI/Ojk7m5opcGU6ya3cX/oBR92eVHDw8xIVzswxsaqXL76Chkaqq82PLRUeGtbLmb7SKtuTtSclfbFVriOXKtQ0BltSWmNbUMHyZlEwXJe3X6DVkqsqj+HxaMhT/p+GuAvSHNfrDsDJRDZQf98/+4Sxzc0WkVO/JlVDMV3nht6e578FB3nt3BACfafCdZ3YzfGmB05fSCCAeUPZTZxw/GyNKcAssbgRu/U7zb7X51VRorUqWXT6coclF1TVF63Gca69ZX9fy+hqcspxk/S+5xpKqy+I1NHrXeKhNFFTX7I721b8bM9V4ZWm5Ejxh1a1cZ9qH6TPw+I11k3xafJDohOMzyt7n9jYlsvHqUHLgUP8ix6+Zqf7yEo6dZFUHbaqoHlCZGiTWGAPZUnV41xJCLTVn71vls6dqNAdvT0ra/HA+3RDHSPKWpOYKRnMSzZAky5LuaAhL6NilKqVSjf3f6Odn//AFui4Y2BjH9HtYmCtw3wOb0DWBz6dzLg0XUyqtKeJVXMv2wOKo+0arLyTq4iJ1Tj6dlTjUj1coLqlHX22WvbR2JgS5Grw14fJgj6AzIHigB86lFBCMmi4JU7AjDi+NSNI15TF4sx3GoCGIelWXcq4Ed3cIhrNwtaAiZg0BbVGdN/JRugdjpAQENIkHBUqni5K5CuxuhVrZ4dVfn0dzHPKuEneFPaALh9dePs+Rp3YzX9bX7VqsVbe2aVzIuHw6dzPgdPnP3SxAvV7H6FobNtuWVGtVkJKHD22jWKiSy1SoVG1i8QDVqsWFc/MMX042k3FcKXEcl1ymgmO5/OJnpzh4aCuaLhi5kuLz35dp7whxx/4+Pvl8GqEL5ubyFAtW8x40vBpd3SH+9X+/j4vn51fxMA8c2opVs/jy5BS37evFdZRN3Tc6F89Xqu7I9Pm85LYlYpGHezX+7pzb7HpHTcHBfjU23hbTAMlwFvrDSjW+Iw6f1LnnDaGa6dc5fHSIV148R8FV59utJ2EdqncfhQC/38A0dfK5RXAqkITDXlzHbUY2qy0svPHqJR54eBDDo+M4LnNzJUyvwW139CqvzDeHufveATwejWrFbn5+tqUSps6fmycU9lHIV1mYL2J64eA9UcyRE1gLI5RtMDq+xHPwVn79yy958OFBbrujl/m5AqlkiXvv34iuC4JBL1+emiUQVKlzW7tXq8tXZs2v9Dm9Vlmu5PiM5PGBRUW/rik7uRutVp+iFK1HsbFc1ekdiMCdneKa69M/VQkhmJnKMz9fbJ4vt359WJarAKsrCUdMctkqhUKNX/zsJH/1393O5+fTmDqkSja/eu48Tzy9m87gEmhU3wi0DdyBJhT5vmKzaioyW1YUq7KtNuWxut4jXaxhOS4a0Bb+umt6o/VnD05N02Tjxo3/3IdxQzWXqyHRiAe9a+7A+sJwKat4WkGP4K4OmsBQIEj4lPXFPfWO0tmUS7BFGcSniw5Bj1wG+FbyAdfrAgmUl2qqosBepqpEUwf6VoujojGTx57YzuyM8iVsawvR2b28g5aqqg5KaI372HIVwX6lQvJmaiAi+FFEcHLB5WJaMpxTIrGAYeDINlo0CKF2xx6PwTPfHuLVly7w0bEx7r5nAz/80e3MzxVIJkskEgH239mHYWjYloMEuvySzf03ZrR+vQp5BKGoclsYzcNtbWpTMZKDj0oqderuruV8z5X1jU7B6RR8mVS8sZ6giuTsDSnvzoSpolAvZyUHO7/a6Btgfwe8NAo9IUhWYU8LvDWhqBaf1e3NcjVJdwAu5SRtfsFrsy41F7ZEIeaFz+cF26wCXuniNQQBlDAjX1PgXhcOhWSBi+UoB/purhPxUK/i1n06pwQa1yrD8NDRsVoFdjMANV2VtF5n7NncsK0QRQWDBnffu5G3Pxonly7THjPxBzxMjafJ5arsv6ufufkCUi7aDAE4tkss4efChXlKxRr5fJUvT89w330b+eC9EWZn87z30VWOPraNbLKwahrioqIuo1GTW3Z3Mb9QYH62iOnTeerpXVQqFr/+5RkMj0Z3dxSkTa6mqBhHNqj32uEXfGuT8kqeKCggpAkVtQuKs9joyrf5FpX9T28WTBTUuS3byl2h3Q/3dC3u6hq2bE//YA9TkzkW5ooITeDzGXxy/CrJZIn29qDKqn9/BHQV6yw08IdMevuiPPvr0xx+bDuvvnShSW+Ymspz6tQ0d9+zkZmZHKZpYHp1XFfy9pvD5HMV2tpDmKbO5OQotu2Sq6dkZbMVpJSEgl5mppSHllVzeO2dGZ7+zv+dUOY8Wk0SMICgzv47+/nVz7/ENHXaOoII4PSpGe65fwCkZGoyRzJZIhwx0cQa6+4SJft6KVHr1VwZTG15XKjr3pzpfEcAtq4R9dwojyZWdc3/2KVrgvm5wuKzynERgKxfOlIql5nWtlDTCrBcsliYK9DRHiSdVJG85YpDYaHAx9UYMVMyYBYpvK3iU503/wORb/8tRcdgrCD5zmD9tVHTKI8mGIpDu395F2IsWUZDidhi1/Ak/7qW1589OP1TqYq1+OBKhLxr/oxA8Eiv4pm+NS6ZKi4neO9rEzw3KtnTqjqTlzKSJzcaPPnEdl556RzJooPlqjGbr27MfzMG8QmfWpQsV3Fc35qUBHRJoq727HPKvP2qiiQMhlTk6cTVLPeFN2H6FzvBtyQEJRvmyxq9oeWWSyM5xVv7Mik5nVTWJ7omuKNtfWX/erWnVWuS/K9Vmj+w6FGaqdDRESYSNQkGvRgeDSnBdlwKxRqt/iA+j6INOFLWY0eXH1fVkdSca/u4rqxWPzy9eeUoX1C2leglU4Ht1+gI7ogrXmiqoj6noAf8unI92JkQuKg8bVd+dUDt0ZSa/cukpCsA7WElDHphRAnjnh9R6nWfIanYcCGtxB13dsKLI4oL+9iAxpe/X87D1BFEvYq/6UooZMsEojE+mJbsa7+x1CpQoOnxAXhxVDIYXe4Zu7TmyqpTu97r3ihAnSlxXe/d9Txw460hXnzlEslkmagXioUaxWKNWtUmEPAQCpvks1VWNNUIBNR9pQATymJJVyrwQMjDdKpGbqrA+HSBVDRGQY/R0wEeczlz1GN6SCXLeD06oZCX+dkCr79ykXy+SmdXmKOPDRENGxQLNod6dZ4bc7Fc2bw+46bgr7fBf74g+U/nJQf6FC3ltjZ1HT69Wf2c4qlLslXVaRqKw9mUJORRSW5bY6s/Axfw+HU2bUkwsDFOIV9lajLPLbu7aG0NEIv7efmFc8wvlEDTcBG0twc58uQOgmEvDx/cxpu/u8jBI1splywymQrxuI/tOzuoVCw+PT4O0DTm9/sNnv7+HrymoJCvkEmVKZYspFQgyHVV5zYQ8DAxkSWRCICmU5N+ZnIm+cQD+A1JJCzQgY4uyZHHh5RncrbcpFa0tgZ5641LALS0BKii8faES6YiQSgx6gZfifDrP6ZQlc0I5NTbPyb87b+lagSvaau3UJYcm5a0rmCE6XVbqqIt17VtW1qaENdMZ1t6HfyxS6I66wtFCIbN5meCBKGJZfdHKGyqlXjJ11LJEu2tfqZnFxM2ZubLpD0xpoqwOTxLcm4cvwG1qyeozI3ycnUHrSb84pJkS0ySMAWFGjw2wJpOIKL+DHCl5Nx0gcG2AD7Pn1bozz9H/dmD01qtxuzsLB0dHXi9a4O+/1plu5L5fI1c2aJQdfDqGiGfTlvYy+hCuXlPXc8rLeQR7GiBK1nYEFVjdXW/OkS9yj/S1EWz29bSvhgVODpbpqPFz0Dvzac2NcqjCY5ugPmK6oz8dljyaIfLz3+qukNhj1AP24Liqi31ONWAQtHBn8ozNVpGti3v6lQcBYBubxO4ruRKTqksf3ZJpSBdr1N1rbJtm0wmQywWwzAWz3HjYdg/mCCfLmPVJNWKg89vUCnbpBZKSKCtPcRcpsr5pJ9s/eHx5CZRHxwqgv2pBcnlrOSebkF4neNoqPKX1nq2U35D2Tz97qpkpgQP9cJa3FZNCPa0qs723V2Sh3oEx2bUwj5dlIS8yvbnekp225XXtGLamRBczSuFNShA8v0tCoCcTkk+nVXH4NXUWMy2JL8dVuB5e0INCtfiYQoEddch+tr99HULpgpqSnBnx6Ia+HrVyK547kojiWZ5XclJxnKQKlvcFsrR0xpF141VXXAhYDACx2Ykt7apbvRSgHp319obk7WqcX01qDPVOifwwIMbeO2lC8soMMGgl7vv24ihC7ymQWWJP6ffb3D0iSFefflC8wEcjfq4fHmBVKaCY3jw6jU8GliFMvdvj+OuE2Tg4NDe5qdYcfDUR9377+qntT1IV1eYQNDABdKZCtGoyU69imT5xaNrgh9tV7Gw59KS3pCgPUBTnNaojoCi2QQMZbjf5hdkLJWxPppzkSgBZMRbpxGZEPRI0DQ0HXx+D63tAXw+nWDYSyDoUalRM/X8+rYQXd1hwkEdG2hrD3DksSHm5opYhsPQzjZCQR+ffDTG9p0dPPXdW5ibK5JcKJJoCdDVHcE0NWzHpVZzEbrAddRx2bbb5ON+cvwqju1SKVt4TRXvnEmX2TMYpxGz7gAtbQFM08P0dA6f3yAQVJZHH7w7zPlz8/j9HtrbQwQjXlo0dQ29NKY60L75UeamTxAzVTfQciF75QRidpRj7EATkv0dYlUELyhqlyOpiyYbnFEV1rEtpsDWU5sU3eKr1mhO8sms5OFeSau/sfKphkLZVp+jjhKzNnRBJauuf6iqNWi9TePSmi0pT+2CpV4rbjrc1RnG9OqUSnVJk5RNr1afz6C1Lcj7744oGl/dizrREiB1aYHWJfz6rRtCxBMOc1NZTp9KE933PxINlrE/+j+QH/xvPPrY31LzRIiZyn3i85zE1JUQVGkLltdAa4Azkw6O4zKft9BEme1da/zg17Ws/uzBqZQSy7JuyhD9Dy3HlQzPl7AdScink6/YlGsu+arNlZzKJFY7T5dk0WIsqcYQEo1N7UFM4/rkxW1RQadmc+lMilSySGtdofjERoMXRlxMXbKvDUAsezh6u+KM5iRb/Cs1uDdXmhB0+NUYtzeskV/I4JXKrirokatiLGdn8mzYFGdutsjPfnMefz0y9SzLE6RmiosEe00TbK53ViIeeH9ackf7aq5ppiq5kmt0sdT3ruRUvOLyUibLa7EFh7MSKSQL55Th9t37eshmyhSLNYJBL7GYn1NfTKlM79uDSOk2/1bZlvx6WBL0qDHvU4Prf37jBclwBh7svfFzHfYIvjMoODYteWFU8kjvchufRrX7BcPZRtyr8l99cqPgd2MSS6os+7U6HmdTEl1TndY3J9YGdUtLopwflpfgloTg1LxLrqZey5HK+1UTyjZsa0z9nRsVznUFBUc2KOpAwYIta3TZVtaOhHpInkrCf77gsiMuiHmh7Cjh2sWM5GC/IFOCNy5YREsuQpfc17X40L5akIzlocUUaELZ8eSq6pw2AOrrVyXdNymillJSdRRtZXeroLXesb86mWdmoUxbi5+NvWGqFZv33xvh0JGtFIs1Usky8YSPgYEE7797hfm5AkKIpkAok6kSj/lYmM/jr/MMW+pCv6Vlu+o8xkywLBddgtcrCIVM4jv8eAydYrGK40hc10UIDbvmYlkuZz+foKd1ULXGl1S2zj99qO5X28BMDYN+UFGbZ1MSry5o1RzS8wWMTJm+JaI5W0qK9RCFuZJkoQJHN0B6fp3EuSe3U+7soH9QTW8yNcmVpGR3q8bI1QzPvXSJ7YMx7r9vAMeR/OInX5BKlblyJc19D27C7zMY2JTArAecVCoOtZrDe+9c4anv7mJsNE02U6GrO4Jtuxw/NsbcbEHRexyXWr5KJOpb08PXlhKfD2JxH8OXkkxPZZmaVHQAv9/D09/fg+nTMH06jSHw3Z1gOg2uaZ2qJRS336tD7YMfs/fo36L7giq1KMwqIZKhCZ4ahEsZNd5vX7Lh3RhRlkq/uSK5vV15J38V8U5HAAajcCkL2ZokW4OJoqTdL/DrSvdgS6jaUKrzFQKGoGhJeoKChYpsJgQOxQWDsUVtQa6m3Fc+n5dowN1dgp4lm1K35uXwY0O8VPehlpL6faBz+Gidt52tNAFrYxPw0Ydjzddo7whh6Dq/+ukprFIBK3kVKSVev5+jT/6PGCf+v7RVr2LFdgHwaJ96rY9mJJcyKgSl1Q+3tS2u8VG/h1v7o/y+7u4wn68x1Cn/ZLQu/1z1Zw9O/2tXqmgxPFekWFPgZS5fA9QI8HJWY2NcpyfuxfRoaEKQKVmkizUEEDCNa9pHNUoHZqaK/PKnJymVLZUuIiDg9/Cd7++hPxQkYbokVnBjpJS0+yBsLLfR+KolUUrx/Z0a0xNlBIKwV/EHXVRnSUq1uM4slGnpivCr36owgKWdqkaC1He+v5vJouC+7tV/q9UvONCnLJRWVsxU7+Y3w5JdLdAdUoDLb1ybq7m0ekMwWZK0t/k5dWKC/zycZnNfECPgxy2lm+rdwS3KTNV21d+ousoC6mD/8oV0rZosqsVtS/Srnfu7u5QISVmYLP+eRC3sd7Qriy9Dk5iaAmR5S3UxBiJrv26bX43CG2lSb0243NctsFwYL0DNVibpt7UrcDsQVkD5QN9q8O8zABsiXmV6XXNhfztYUvD8qOTJjevzMNeynjJ1Jar5zbBKGrpe57xoq4ehR1Oj/a4gWA4kPErMBconWF1PgpYWDQvBO5OSrTG1YZvIwwM96uE9lIDj05KRnCRZVXzugYgS1fXdZHPErvOpdyYEHX61aRytaUz4o9y1L85UUaL7JBG/yaOHtjA+msFxXIZ2tGFbLs89e4a5GWXH7g8YHDi0lY+PX8XnV/GNpYLVPI8dnapvr2uiKXJ8Y0LSFxLE6iJEj0ejVHKwbYll2fh8HmzbRQiBbbuUijVqls3sjEVXm5/k7OoY2iv1GFNDE6SqqiN8sF+wUIbzGUmxfs57Q4JEtcw7r54nX3Yo2XDRFMs2pmGvsqkCwXRJcmXe5tN1Eud++quzfOd7e+ioS6pfGpU8tkE0HUocCdPjWRbmi3g8Go89uYOXXjhHKlni1z8/RSRi0tsb5Y67+hSfNGTy0vPnmn6qv/9kHK/XoLcvyisvXmxyV6WUGIZGuWYp/8yuyJqND7Mwireis29/N/PzcZLJEi2JgOLf6xZhMUuF/ubP94cFYm503ax5Jk5gZkeJRHdysF/w7oTqVt/d2bCZU+XVxJrpaAAbwoK/2gafzEqOzcC9XTe/DvkNwa3tCgC7UqXY7WpZe9O7/Bkjlv3fcSWnU8oruOooMFqwIORV3qlqGrP8NT2mTmub6ooX8lWy2QqxuJ9o1Ifh0Xjr9cvqtR1JMOjlO8/s4vPPJxbPjVfn/oc28fxvzmDVajj5eYThRQRaqbgaz/9uge985/9GPj9OtK1MVVt8Fqt4cLXZ/nxe8umsy9YYBDyCubKiFBm6hu2AK12ms9U1HXe+rsX6Gpz+V6hUscZCwSJdtChbCpTqukHYZ2Dogks5nXBU5/+0XSe6YmTfl/DjuJJUsXbdcX6+pqIrW3H45U9PUq5YCKH0qK6EQsniFz87yb/79/sxg+u/1s1yN9er8bwCaW0+qNRHtaYm8Hplc7wkULvpcMzPzEyBmuVguxAwZN2qQx1LreYwNZVnMBYn4Fk7+jPkEWuKqEBwaxtsjass59evKl/UdyYl22PKoH+tVKGlZeqCTWGwjDAhn07RcpiZLVK0isTq3NGlXb23J1VnTzrwnUFxzeSRRnUFlddj33rz/huowahKjlqqngY1/ntjXPJwr2B/p+C9Scn2uPqMIl64s2N9VW2bX/CX2xSPOFNV/qr/eFFiCCWkyNVgQxj+4YLkO5vUw21fx/LXcqXqHjuu4riZBtzVqbivJ+sWT9vjCkQ8vnFtHuZamfEAulACnBsZfuRqykXCqyl6QSOYAtS1emJ+9Yv4DcHDvSqVy5GLwBSUU8LdXXAuBSN5yYdTknu6wZLQ4r/+hGPxby8C0wZFYbooGcspCoauSQYj0Jh0mD6V3f757ycZG0mz97Ye7rt/I+NXM0QiPgIhL+fOzpLN1ThyZBunvpwGFgG+rgvGr6SbccXRtjARr7bMyaBiu3g8OrWqTa3mIqWNZTmYpkGpZPP8b8/y7e/uYmI8S6lokUpX2CCWC3h2tcAXC/DrYZenBpW11umkxKcres6nc0pIuTfi8qtnzyMdR60H9fu4sTF96nu7cb1aXbij0tXmxgrUajYrQUrNlbiOQ2EhT2dcgeWwR3G8XQntLX60+jk/fuwqd93dT0urj+//5V7lcbpQJBwyaWsPoelgmgbJhSLZbIUDh7ei6Sg6T7LM+++OcOjotmbUKwgMQ8fwuDx6aCuhkIeKvRw8e+0i2Tf+F2Qxh7j1vyNstBJu9+Px5NBnL1E9/r/jBCPLVPjXy5oXQlB+/8dEnv5bNG+Ih3oFZ1Kqc7meq4XjKkrUUtW9R1NhCW+MS54fcfEbsL9d3NSoX6vTBP6Q0jVFQ9rTqqY1C2X4i22rAenScqQkGvORSVcIBD1ohoZp6gT8HkIRL/fcN9BcS9o6wlRrNomWIK1tIRIJP13dEWamctRqDsKuIV0HN9BBLltpxqeOjOa4cnKch7+1gUS3f9lapGuCgAZ3dahr/MUx5ajSG1IUutsHYhwfTiGA8rUMkL8u4Gtw+kctx5VMZ2tMpCtULJXqJIROLOBhS2cI09C4mpdUS5JH+sWao1hQF31b+PrRGgEPfDwn2FjINhNQQAHTBkitVSwmx7Ns3tHWTMm4VjUSNNQQTt5UJnyhJusdRJfNS0a1AmUt1DzugM5gX5hzZ+aIeJQ4J19TD7kGhpZSsjBXoCXiMF7R1wUq16qgoVKidrcI8pYkW5Ycn4OzaUmLT7IrJtfMfl9apl/nocNDvPTiedz6Q6doS2KB5V29kq2EXTdTsyXV2fjqYgIF93e3qs5Fw5cQ1Ove0a4A+dENGkNxdb1crQvMWq7TkPdqgicGBLMl5YPa6oOypT6fXN1IXSL59RW1iDdAju1KJovK7zDiVd2PiAdi3kUf1f0dqqNry8UR3koe5lILs7XqRs9ZV0DQFVAc118OL17HFzNKcLVSLNIoUxfsWjdhTLA9ofh0l7IKoKpR7o1NINYDpmdSym92LdeHatnh7TeG2XtbN6++fIHR35xmYCDOHfv7KBSqVMo2O2/v48HDIYq5Mi19LezbpwB+tWLz8388udhxFCAMnUePDKGx+MDVUOP9fK7KhYtzbOiPszBf4Pixq1iWww/+6lYunptlcGsbH304RtfGllUdY4G6Rva2CjJV9dnviENfWPDsFZd0VYHG3HwO3XWwpOriV11lZI4AqjZXxvOMB6L0BhU1w9QFxWyZsqMoIhJ1beRrNNeWUq6MJuI4UvkCvz7u8lCPoKMzTFvYIJ+vsbBQ5OPj4+zb30t7R4ANG+O0tAbI5ipYtkMs5KNSqZFOlzl0eCvnzs5ierp49PBWXnvpAnOzBY4fG+Pgka1UyhZCCLymjmHoyq92rbWyrra3XUnxpf+JREsbeiBGtZAmlVkg6BHYCyxX4a/Iml/zOrp6ApkcRXSp3+nwqyjjkq2eAQlTCSsvZyQzZfW1qq02W0s9jX262oxZjroPj89K9rZd33D/n67UfZO3FH9VSvjelhvb6BlejUjcx7HXpxC6Rq1iUc6WsWoO3/7uLRRKNS5fWuCjD8dwkHj9Xu59cBC9NYzXr7rqAkd1TQOtTWDaqGy2guExeOnnH/K9//4QRmC1TsXQBHvbBEGv2szvblE+319O5JqrQfxrI/7r1p89ODUMQ1nGCI2FgupO3gin81olJaSyNtPTBdLpMvG4n/72CI4ObRFzmaDkzQnlt7keML2Rmi6p8djmqEZvWDB3pQhyOXNy6fMtmSyxTVMWIuuVhnoAzs3kSSbLhEKKtO9Il3g8cE1gaLuSz+YV0f0bnYJTScnO+PVGtUZTCKMhiHglqap66JhI8tkKQtM4fWKCStFaNu5rHEfFUaDgRs5l2CPI1xQf9PGNCnx/MKHRH4jSpq0PUV3gjOPnB3+xh9npPNMLZbra/PQts8O6vqn1WrX2KPzG68Q8DEQkQ3FliL+Sg9kZUODSWsItHMmr9K5rqXyXVkdA8BdbFe/vpTEo2JDwqU5LWx3gPlJ/D18sKJpCd0DQExJYDuhCdZQ/mVV2Yz1B2BxTnci1mstSymsa2X/Vylvq4Q1q4vDhtPojDV9OTdOJxWJo17gWVlbjfF/KKjeC2dL6XatGXQ+Yrq3+VbGnc7MFLMtZNsbMpFWaVqLLx2nbz+fzLmFPiNhAmLZ2oObwwrNnm/eg60qsmoNl1/jdc6f5wV/dSiBk4KDu/1/97BTf+8td3BHpY2IiQ81yOXBkKz09US6em6W9M0IuV8F1HXq6gsy7i4D8UkbyZVJ58Z5NS4IGfKNLEK934b61SfDymCRsCqavlKnUeb9SKhu5pZ18p1jm0FCcZ6849IVgsgSJhOJzZmvqGlH3/uL9H1/C97yrQ61Dr12VPDGgNpiv/PY01bLD6EiKfK5Cb3+Uu76xAVdKOtpDZNJlxkYzTI5nmV8oMDtT4Ad/dSuvv6q6pIrza5HNVigVLRItfrp7IvyXv/8Mj6Hx/R/eppTjS2ppB7RiKyN3J7+Ak19obv4b1fAwbfz7eiWA+bd+TNczf4vjCdHqFzyxUQl1bFdZeA3nFL90R4u65z+ZlYzk1T28tAKGaKKDQ/1/fFBatCVTBcW5nypCh18FrdzeJrjlOo4qNVfy/pS65yQghJ+jTwxRTBaYWagQ8usgBO9/NM70fLnpVKIjcMoW+YU8C/4QjlsXY9o1pGNhu1r99eoXJZJo1MfMxQqVfJnZyRS9W7vWbdaYmrLIA3VcxaqNhkvI1L9OibqB+rMHp7mqy1TGJVXK47iqwyhQ3cot7QHaIzcQBrykMmWbbLLGSy+eV8pOAZqmoXsMHjkyxOtZ2N0qm/zDA31wbEagaSpi0HHlTYuzHBe+mIffz7lsjmm0tgVx6t1SKVkFlFpaAjjXAabJOSU0KJWs5u7RNA0OH93GZ59MctfdG5YBw0Z9NufWxUeCTR2Cvz+nogrXs8xpdECdFYbkAkHClLguJJMVTJ9OIGRSKtbQEM1xX0PlD8qoPlMV7OsQN9Th7QzAXw4tjrMf2aDz22GNvV3X3pxsjcLJnKDqj7JhZ5zByPKuXqXezbmZathOrRz/L5SVijnguf6GaUNYqfa/t0VDE3KZNczr4y4H+jS2xASTBZivSAbCgoHw9QHU6hK0BwR/uVVyKqk4w7arImcjXiVIS1aUUOjpzQJTh49mlKiswb3dHlfg7YsFSdkW7G79p+E532i1+wV3dsDfnVOfmleDZ7aIZhiFpmmY5s1zwrbEBLZUfOPJvFzG91tZXwWYwvLY03SqzAfvjTSt2ebmChQLNW7f18PhOwKkK4I3xyUXM5J7u3XGr+abwNSyHNKpclOxXyhUOXt6loFNCdo6AkzMqJ998bkLPHJgM/19MXp7o8xM57k6lqFvQ4Jq1eKdt4Z5/MmdZNJlqhHFSTmbcjmTUpZ193cLouZapu2CoxvUqH8470fCmipzUNHGoDqwH8xIdsQh0R0m6NOR9QmG7SpwC6s9mgGQimuctySXpZ/vfm83czN5JieybN7SQktrENt2WJgvIaWktTWEx1Mj3uJnw8YY8USQ1jYfjxzcyi9+8gUvvXCOcMTE5/NwddTl8GNDvPHaRbxenaNP7mAeg74V72Npbry3pmzQlt7a3pryqNbrE2yvpq7PlVnzS8uWylM3qkHd3bP5PV2IZtLRhjVy43264nH+U9VITtnorRabLlaqIrmUVf/OVCVWXRS5MSLYFBE81Lu+wf9a5dWUheLyMqh5YmS8gk4ny6+fu6C+qkHRUjSnSB0fxuN+whHJyQXJ3R0B9FoGx+PHkTpSeOoAFXx+g1DIoJBT915yfILBzTEqYu11omHjKKVkbKHUfEeb2q6vG/m6/szB6US6wsXpPJVqDdPnp+JqDGeVaXhXAM7NFAn7Dfw34ElWrjlcTVUI6h5eelF5eZqGRskR6BI8jsMnb1zgW8/s5tcTgvt7YCAk6PbCI9Eq507lKeartLWvNqW/XvWGBE9vUeDWlVAJRAkGPJTLFpLlXVO/30NPX/SaI/1qWQG/atVeNtaoVm1efeUCR44OrQKGjRrLw1ODi5GgW2PKvidmXn9Uu1III4TAsW38fp1HD2/jy9PTCkjW30/NUir/vk1xhJTEcKmm83xxVfHorjf6X6lGdR0Xv1vCcQLo+vqfeV9YdSmHEjAQcpErvEG/XICBNR4ES+vdSck3uhY7lp/Pr/07ZVtyfBYe7ZPX5cXGfeoBnbckLT7BbAk21UVOuZrqLIc9qtu5LSYYLygF+kLlmi+7bnl1sSyNbG8r/PKyxJWqQ7W/QzBZhI9nJdtjgu9uXrS40YTy401XFy1urvP2/snL0BSd4Pdz8EDPcjqF6zqUy2X8fv9NdU8tVzKeV+Ks4TzEfJJNawjcviowhbVjT5daswFNlXjEK/j2oLJzKjuLoNZ15TJg2rin0ukyw69f4tvfvWUZAJ4Yz9DTG2NuJo/h0QkEPZQrNvlchSee3Mnnn08QjfjRQxFeu+rgAre3wkBUXFf1rQvY2Ku43PmaQ8izHJwsBZobwqIJsjR0nnxiu1qranYd5Ik1hXOgNmSDUXW9C5TNkFm34zr24RjPfP8WKhXlclCrORSLFbp7IgxsjDI9XcTwqE12V3eQf/Pv72RyIsvCQpGWliCdXWHm5/Ps2ttNZ2eYYMig7K7ecC3mxkveGJE8uVHDXvL90bRLyBBNYFNofGONrPlG/cMFlwMDgnhA+R7/dFTS4nPZ09IQCK5//msu9Wvgxm++iqOimaeKCty2+FRYgqEJfLrij/cG1f01X4G4qda5vKUSwM6mVAhG3FTrzx/LE3U4I9kQlHSYQVqCOo7lAMqlpLFnaVxbo2VFIYn6bR575hu8+PIIlbylGlZIvD6DRw9t48sz0xiJXpAuLd2dqBnDtWs8VWYiVUbg0hrykAj+81pW/qnUnzc4TVVwEFRqFpu74/S2hthbkbwz4VBx8gSEw+9Hsgy2B2gNefGuGPfXbJeFQo25XI1M2cbn0bGqFq4tCZg6RUsJRsz6zVerOSTn8nx/S4xP5yT+SoXcfJGXXzxPuWLXzZAFrW1BHv/mjjU7kyurMX6fncmTTJUJRPy0dkf4zvf38IufnKRSsZrd04ZNSSC4/i3VGBnWLAfLcpbxbQCqFZtCvorHqzeBocoxVtzUvrBgriSbD9y7OuAfLipldOMhtd6odnV3tYJp6miaxpnT08zOlwno4NhKQWwYglSqzMbBOHOzZZ59/hz5srNMoLRy9H+t8usuPXqBn1/08mC/RndTXS/J1cByJC1+DYHKelek/9UL66Ws5ImB9RfcgiWZLEryNeXbCAqYPLRq9w99YQ2hSX4/B/d2XfshIlDCGcuFjRFFGWl0MMIexS8Le1XM7fa44p71hUUzy/wPLU0ISrbyW606qpOarMBfbFkNtqqOGqU/vUXckPH3H6NCHsG2uOp0966wH3Ndl0KhgGma2Gg3RHuwXMnbEyq9rM0veG/KJVdb/Zn9IcAUrh17Cmt1DQX9YcnzV1zuiPkVF7LmrAKmSIhEfVy6tMDURI5Ey2JL/ctTswT8XlpaQ+RyFXJZFWva2RXlrTcucf7cPAcOb2WzXmNzj+emJwemX+fpb23nl8+eI1t18OnKO9K8RhhIY7149KldTE4V0EplEtfYlHYGVJe2NwDpmkY6WSQY9LJrbxdv/u4Sf/cfPue+BzcxMBAlHPFRLNYYv5rF69VpaQ1yqaKzre7za4YMtuxsY6feQU1KkmXA46diS9Kag8m1edCStRPwBsKCV8eUHdGN0JPSVcXxfnlMcniD4lR/d7Py3/xgGry65L7u9TvS/WH4YAo2x24coJZtmCupRDddU7SBT2aVA0jQUBzjd6fUtRU0BJmaBKnEV1uigh9sFTfVGV3rPc/V/Yr9/3/2/vtJjjO99wU/b2aW913tLXwDIAnQgt47ADQz1AzHSBqNdHR1dSP2p93/YXdjYzdiN2J/uDf2nBvn6B6ZMRpLNxxyhp4EvYH3QPvu6i7vKzPf/eGp6q72Dc5IOhL5RDAAortcVuab3/d5vsYUHvtgePXU6XJe/Jt9AZMnnty32PQwDVkLPV5j8dzqMTW9QSipINEuP3ffZ+C6mrGrGaIRH4GgxQfvXGI+XSPREcTvt+ge6aOoNj/R23HDfLHBTK5Gb+zaJrJfxfpKg9NowKJQqePYDcbTFcIBHz1RH3f2G7wxFiJWK9Lh05ybLXNhrkxnWCyeSjWHasOhvKi4U6BMOiIB8uksAY9JoSG8Q9+KBSqbqTCyM8EtMYcrV0q8/PwZ6jVb+EaucCxTqRIvPX+aZ79/cFlnsgUAWx1HA5ifK/P882coVx0qTQ5TJGhy5JvX81d/e4jZqRwLqTJdXUEGBmOLfLL1qn1kaNtCXNVaL0o7lFLkclUCAQ/ZTIXtKkG5LOA4k64Qjwbw+yPiEIDQI67r0Pz6kuYbOzcf17R3V3s1zI3neOXFcwD4lSadroJu43RqKOTr/OaFM5iO0wS98m7XGv1vVttjit1hxe+nNU9uE6V2sSEJRn4LcnWXb+9UWMZyuUvNEcAdtBQeJdyp/esIosIehdfQjBdYBKfdQUWpsaRSbq/BkCJV1nyaalnprHPsmmCkwydWRq1xoYss5pYpcaURD4wVpKuyPyEBBjujf1iAAcDZrLz+y2Nyw+z0Kx4eXNsv8c0p6az+WwHTVnkNxW3d6/98pqR5bUbzyODGRv8rgSnAbd3CqRwML8WY/qHAtFXXYrcF8PCgwWcpjccI4/OalAr15U+oxag8FPRQLNSYmSlw0y0DiwC4VKxTb7j8H//1I+KJAMnOILWqzbmz84A8NuC3uHh2nusP9LDIj9igsjWXU2nZ0A6FFcnuAH/1g4PMzBSYTslGO9kT5njF5L51nsNF817aYHt3nBu7WtMYF3eFGM3VmrMZiVj1GIpEXda4crlBV3eIb3/3APOpEqm5EmMG9PVDMGixc2cH9bpNIOQhoZ1lPH7Hcak0+VFxC+KWeAh/kBa/5f6NzhcH5qvL/V5B1o/7+8Vxozeoub5DbeiHqbUY+9/arRgryMTPQLEnLpvg23sU5zJwyzrneHdAsSMqnfXOgKbLr7ijd+NzMOFTJFasQzc2eaErr/W6q/nZReH8bt9g1N9emZqk1c2WIdcQyztDgdXc/MZ8ik6/0BkarnTe35/VGEpzf79cQyVbBI4HkqspZTPzFaxwgMH+CMmYXCctYKuUYmoyz5uvX6J/MMr+/V288sJJFubL0uExLUwTDj+59fTERMiLMFYNDNxlaY9f1/r1lQane/tCRL2a46U0ruNwdrqAoWA44uPZvT7+63HYn7DJlW3KNZu5QgNZTloLr0HQZ9ET9dEZ8RLwWowX64AAldgaQCOeCKBRpOdLlAs1ajV7MW6ttUa5rkOxWGdutsi2XQlcR1Mt24sAMNERoLs3Qq4OP/nZGWoNB68h4xOFwq47/NOPT/DNZw8wsqeTmQTs6pPxzWaXRfvI0DQVTjMJpVUKTTTmZ26uSCIRYCFV5YVfi8Ci7oqXZkfI5MgT+xY7lvk63Na79d1yuqr5OOVStuFAJEhNGSjbIZetLnWDtHCAojE/587MU6vZbQvF2qP/rXJ5Yz7FDUnF1QLsTQiY/NNRiZ/71WXpDI5Eln+eugPPXdH86R7xvtxMxNMdhIXa0pee8MnNqje4dgrTTV3SHektrQ+SzueWKBydARnxu1rz2byIlUKWIl1zCVmKkxnNXzStWb6xQ+y1ntgmKuhrLY3mv51e+sD7EnAuBzd1rT3O/mJBM1GEh68haODfqiaKchzfndHsjos10srzeC1gCrKxeXQIjs1oDvVIp+yPAUxh/djTjagsI1F4a9rksaN7+fXPTlAoLJkC+/0Wjx0d5cNjY4Dw0n1BaxkAPnlyhsePjvLSC2cplZZoBK2EpGPvXmXHriSZdJmu/siG11uupnnxKtzVq6g5S5/JEzAZ3pFg284ErtaMFTR7N6D+fjgHC1V4ervw9QFeGtPMlGR6cN+AgLVzWZliBSzFTMbm9Vcv8PTjO3nv7SscuLGPYNBDd2+YjqSsz9p1qVZsDANMS9aTjcCmlLg2DEchs4bfcnt5TUVPQK+5IiYDigcHZAO3UJVreb3q8Cu+tVO4tvm211TQdEKAW7o3ft83dolAymOIX3CmxqJobau11gbURdT2Q+GtA1MQxwWvAbf2LG/uNFwIrCNW3pdQnEhrTqZhNKF5fUI3AbNqvhc5t/x9caqBODGPZkHDSq1Ve3NmeiJDZW6aex7YS6HYIJetEov7GRpJ0NMTpL5Outp6ZSDr+kDia3/TrdRXGpwaStEV8bK708OsrWjgcnWhQmfEh99U3D3kJdXwcvsI5Mo22XKDuu0SC3rwe0xCPhNPW4egNW7zeE3sqrPqJtYatyk05VKDXLaC2wKmbaU11GsO86kSju3i81v87rfnaTRaWnCNJxzgjlv7cR2HkMUqPmKj7lBKFwn0dLA9IjnmLeXgWtXele3pjeCxDGrNf2+/yfj8Fj6fRb1m09kd5uc/OU697tBwxbIk5oVG3V3sWFoBg6sFeGiNkXV7ZWuizizUxYz+oUFFhw8UFn/yzX288IsTqJYKXoPHb3H4yCjjYxlcR9NoOHi8FpYSQVmjvnr032g7zqfTmsHI+uOupF924+0pK0oJCG293/bOTMSrGAyLJdH+hOIXlzU7Y3rd0VzUI4rxVo1E4KUr0ik5k5Wuxx09tL2G4pEheOmqJLgMrnGjHC/opg+khBGEPTJe/3x+6fiXG8IJe3LbEhct4hHu6GuTmsPD7a+5tTqZhh0xuL/f4L+edkn6FU5m7YX7nWkxpn5mx5dLoFm7ln8X8xWNoZbb42xW5WaK186o4s6+pX+/tUdxx4CB7YrS+Y1JiX3c1yEgdT1g2qqoV0Ijfjch381IVNEVECW739SczXLNwLRVa3G4He3y80suZQf2xsXft7UORb1gKU0tFOC7f34Tp0/OkklXiMX9BIIePjw2RipVWuSlO667imYTCnn5wV/ewtjVDAvzZWIxeWwrIenGm/uZXyjTMxBZd4M2VdJ8OKc5PKLWjKpsp/2sTHtbWXsT0mF7d9ploiTrV9IPP9yneH8G/uGs5mCn5uM56WRPlSA7VyTsM7hyeYE77h7h3bcuc9MtgyjDoFisYZkGgaCXQMDAcVxUU6y61QpZig1spCnZmjcmWxn3az+xZSg6A7Kx3wictl4Plv9eZ0DxyCBbft8t2spT27fu3HEpL6lIxYYkSqEU5QbsikGuAR/NynX4+PCmT7Wshtfh629motPhk7S4z+blqD68hvNJxKM51AXHZqFqa0ZXOJos43M7DeZyigs/+5xQxEc45OXcqTqmuQuv1yKe9G2pc1qs2s1vWbOtM7gMM3xd69dXGpyCqHIj4TC5skmjvuQpeCKtOZeVjtbfn4MbOz0c6FpNZC7U5eIs25Crg6FMDj2yl9+9eIb2ANBl4zYNwaY107L1u+1/DEMRDErr9Ud//xmO4xKLBzAtg2wNuqIGE2NZIharxj7ahQAOudkCY14Dj2XgrztUzBDxuJeGy6Igqp2z2urKDgzGeOSx3fz8p8eJxvyLoii/3+LxI6N89ukkDz+2h1KxRrVuU7WbxHq9dCNsRZI6XXFWoe8VNVnSvDutublL0RNrGsK3+KlAb2+QJ5/ez8R4jlyT6+YNejl1cpZ63WH37k4cW+Pxyeg/k17uTYeGhVR12WISsOByHg60eVcqJQptpQy6AopURVOx9WIogUKA+odzQuhfWX1BSb3ZEze4sxdem9A8MtTqRop1UgsMjyYUr4wvvUe/KQKBdE0A6gezms/nxYS6VV5DcV1CgHVPQDYxlwtiV2IohQYGmurwU2lNV0CoAiDnMUjnb6q0Or96OKJI1+D/OKv5i1H5tJtVw9V8OCvv+ck2ju2lvF50AMjXNZNFifZs/eyv9i0tzp+nXNI1UXSv5ee5lTq+AJfzYhjeFxKl81hB42g5lsOR1Z+nYmt81pJdUdASwPyzi5qSDX1+GGyeCyBgYTQux/p0Bt6a0hzqlu7WesC0VamK0G2KdZguaq7mpasV9oin53gRRqJ63RCEzaodzBlIIEHJli5YwBJnhJojgGhnVPHpvMu+DovtOzq4eOE8Fy/OU2i23Vby0tsB8HZTMT9T5p9//Dn1mkMw5OXcmbqkJtGiBXjxBb2rIjtBHClSlebnDa8NTK+1Ih5JCFuowJ19wn/saY627+yF0bhY7d3RK0Ct5kJqoYLbaODxWkxN5Hj0yCizM0UmJ7L09EZJJPw4rsZ1NI7jYnkM9DUYKq+kX7VXtqZ5c1rTcOBQz8YgJemX82wtQd1W6svQdDYDpm6TUqUQl4GqI9eErcGrZJ35JCXnddQLA2F1TdMYrUWYVKzL5j3uk2uz4Yq1YMBczS1tVX9IcWRE89JV+Mb2tYNVWu/l5i7N81fglTGXwXBrfVKLDSafFypFTb5QRyuDQqFOMV8T1xify4u/Ps63vnfTunSx8aLwb8OWplC1F7fPQe8fieD/FaivPDj1eDz09PQwfiUHuM2ce4frO8xFE3VXi/H4T5rdoLojN5ZMTXZz26OKqFcAj0dpzrsB/uovDjK33rhNazo7g3Ky+yxqVXvZe1JK4gfDYR/5fI1qMxovl60SigdkcbAbxGNxGg0Hr89axH+27ZLJCi/TUIpf/fwEtZrD4aOjvPP6JR58dDeGIcKHUMgiNbc6m7qvP8K2bQkePTxKpdKgVrWJRH1EIn6KhSq3HRqmWKhS12Ks7W0uGIW6KMHDre5dpsLBnQl+D9Qdyc5eWS6aizmxm1qve+RoqFQbnD8/TyDgYWauwEy6Tqy5SIWbhHXX1eRzVVQbGPb5LUIhLy/8+tQy7ml/GE6Naw4kl17TNMXbEgR43dGreHVC89Q2EEKDiDSe2bn2TWV3XMbAb0xq7huAuqN4Z1o3u5aKn110+cu98noBS+EzNemqXuzwdfg1syXpGB/qgdcmJFnn6IhaBMi74nAqI2lMIKO4d6fhwQEZe+XqMjrP1uDefhbFTq0FfaGm2L5OTOmNnYq6I3ZUj63ooM5WhMPaDig+Tsm5enRkqQvb4Rfw+0QTrE6VZKHe16Eo1JffqKdKmuNp4av9IU3UG5JikJ+vQ7Yuf4Y8inJD8+6MKITbU50AzmbkXL2nrUsa9ih+MCqduN+MmUyHozy6orNiKMVoXPPbMRF93LwJMB0raC7lxGIr6Zf3lvArbu6GD2YkqrbJFvojOmkp5isyTt0TF0D0zrRmT1xxoZk4FbDAjAR4+pn9TI7nJD4zuT4vXWuN7UCt3uCJp/bz43/8jHy+sPjz1mj/5MkZjjy5D63dZR+o2NB8NKfxGIqgBQc28a68lvIair6mXVLfClu0Dv9S2INGfHcTHQG++KTOLSEfr796nn3XdTEwEGVgIILtaAqFGvW6QyDgafpbKubmysSSgVVgs9DQvDeteWBQ4TeUeEPPFpfoVz3hZTQLQ0l4xf0Dm3/ZvQERH221zmU1g+GNhVSzZY3XvPaxfasKdXhzWmMgz3Nvn1o1odgWlXPus3lxa9lK1Zsb3Xxd9A0RryLqkaQlp0mk6woo8nUoNVzxw4VFGypDyd9dDXf3KSKbJPKFPBLWsCsG785IU+GJEaFb+AMmjx/Zy69+cRpXuciZA76gxeNH93Di3c+o1wNr0sXqjrirWAquSzh8dCWP6zgYaPyWQcT/NTjdan3lwanWGsdx2NEZ4PhkEbTLh5cz3LMnuRRRqNSiOXpr56hFK0ShLiC1UJcOycWcJu4F7ybpNr6ARbJTcoBfev7MYjazMhRdXUHuvW8HxWKN+fniIvB0XRfDdfAYFgvZOuGID3MFrSCfq6Ka+c7BkGcxC/rlF8/y6OE9/PgfPuXPf3gL/7//9QP+8q9v5dfPncFu2FhtN24BxA6fvHNVgF/Aw+xsAa2hUmlQLNa4/YHd+H0GHgMCBth1B6PhYloKW5lYplq0swF4eVzzwMDqMbqB4r7+jReS1m620RRmaESR3qpzZ1M89c3reO6XJ3FdGSUpJcD08JFRTp6YWezkthYTryG78XYvUNByjJtjvPdnRZH//izc3iPdAkdrXNbucqUqAqRLtua/n4HbusVaq1VJPyxUxeYJ4GCn4t0ZzdEROceu61D89KJme0w6qQ8OSr70zy5qbu8RIUChLrGfnYGlrt/ZrMtrk/K5t0VgpiKds4ClyNY0Lb9nVwtQ2igz+1CP4rdjMgZtF1F8MS/HXICo1Om05gej0uVuHZNHhhQ/OS9ir+6A6GJap2ixwaJR/2xZ8/sJzTd3SKdzqyN+3RQOru6yygYx6l3+byDX5GsTmgfbxFk3dgnF4PcTLjtjbfZESoDOD/fCP52z+XjO5Obu1rMpbFeiaW/uVvRu4GEKS8A06BEqw/Vt3eMOn+JQr5xjDw1sbrd0LXW1oLmSh/v6Bax/OCsWSlcK8lqt7pEDpDGJb+9kaFQ2Sxvx0rXWJDpCvPP6JX7wV7dw+WKabLa6ONr/9OMJHnx4F/PzJSpOnN0xufg1mpeuag4Pbw4a/thlu3pxg7RQhf4g7BuK8GnA5OQXUxx9ch+vvHyBu+4ewTANHFdTqznYDQePx8TrNTCUYuxKhoOdgVVUhYhHYliPzcANVoUXnztNqVjHtqXjGgp5eeLpfXT2BHG0nJ/f2b01H085xyU0Yyt2S0m/xP8+ONAa8a9+jFJC83lg4MvthmI+taETCcg689a05t6+zXUGtqt5Y0pTsYX7OhhaufFf6/Ht/7b0Od6ddik22JTf6qL55UVNwg/bYwa21lwpyPopHt8lxq5mOPr0PsauZBendcGQh2PvXKVWCWDWs2TTZUZ2JpiriE1bzZaJ663d4uV8YqKM6ziAxu8xuHVb7EvRd76q9ZUHp/V6ncnJSQYGBogGLHIVyUd2XI1hLu24Gw5k6kI6z9al66O17Nh6g2IyvSsgN99io/m4DdJtHK2JRH0U8jX+7C9uYn6+RCFfW7Rvef+9MQ7dMYzf51l2LTq2JhQSEcDJEzM88fQ+fv/qBeF91l0BXj6TRx7bw7F3ry4C22rVplxq4PGYzKdKDA9HOX82hVu3KTeTe4KWLJqlYp1QyItSLBM+1FxJ4fB4TaJdYboDiojXYD5VQrvuomWVYRh09YQW7Wz+cp+Y4pt/wA24XZ1crtmL/COv1+SOu0bobo7+x8dzZDJVOhJ+whEfJ0/MLBLcs5kKI804Q4CkX4DYN3c0gYdts7CwQDKZxLI83N0HJ+Y1RVvG6NuiMiKeKNAcFS+vmE+6jws1+CylOZNVdAblsxsK7utTvDGl2R6VHXtvUJ7vN2MCUE1DkfRrfnxe/j/mg06fdOrOZmUUPRRRnMrI+znUzK8fjRvsiWt+flHjtzTTRYW/eWVfygv3EGT01k69WK/8liKyCPI0jisWMG9NL53MJVv+3lps/+605q/2KS7nBYz6LFHd5mtLqTdxnwB4V2t+O665u0/x7rTEm/7Vvq2dG69NaAbC15bdvTOm8Jjw68ua23poBmAo7u4T0PLquOYv9i4/P13H5uHYAq+kEpzNeghaIrCZKmtu6vzywBSWDMo7fIrrOySI4PaeP85NqwVM720DpsNh8fZ8eHA5CD6+oKnaGgeX8YJsqv0exfbIamutVoXCFrv2dPHic2e4465hQmEfCwslDAMOH93LO29fprcvSiQR51RaHCuu5mF/x78eMG24mgs5cVooNGTc2xOEXA1u6FQETIOnn9rHyy+d4fSpGe6+ZzvZbJl4PEipUKPRcLEs8UA1LTh7dpZY3L8mVQEEDCWUw6//6SSzM0WhFDV/N5et8utfnuTZ7x6kWKwR7wjg829N5e2i6fDBqfRyes96lfQr7uqDEwty/d3Zu/p3ugLSnczWxNHjhs6N1+XzWc1QZLWmYcP3rSUYJrhBCJJG88V809UkATtiW+Nh1hz5bosNSWBqaBEajRd0cxKy2fvU/PS8OK880GyK7I4rbBe+WIAbwg4v/PoMt981wvjVLB++P7aMvmKaCkNp4uEw0bDBm1Ny7fgMCTO4o0ctBm/Egh7SpToKRbXhki7V6d5CDPnXJfWVB6etclxNrmKjMQh6Reh0Pqt5u3lDDlliuxHwiOjn6Mhq8+BiQxJzbtvA7qdVSinS6TLVmsMrr1wk2RHA7zM5cyZFZqHMY0dGefftyxy6Y1hG/83OqtlEZQmfGG+3bFBmpwtMTebxek0CQS/vvn2FudnistfM5aoEQ14WFsr09ETIpss06g7RgEXFFi/PgKXxGgJ8Hz08yhuvXaTelNN6DfA1M+TPVk36wpq77xnh+V+fptamNvJ6FXfePYLZXNAU6g82WG9XJ49PiadrX1eQnubYzG6O/i+cn6dmWEzPFaiX68uAWHsnFySj+7WJ9cHa7piiUNMcX4BPU5ptUQGe62Wse5sihu4AbPO5XBrLk5qtcL4eYMdghETA5K5eeP6K8JF2xBQHkoqaLXZOdVfT44ftEXjhqiRG9QQg7pNkm1RDvEmFMyzm2xEPpNtUupfyAv72NYVc3UFR4h/oVHw8K/6JYwW9rugAYKIotIA9cUWuBj+/JBYtdtsddbyw1iM1HzZFEKaCX14SM+6Wf6vfgv4Q/N0ZzW09ipEIfDDLujSDlVWyBWysFDFspYbDit6AjOI9hlzLAENhg4TP5XxWxDXtpZREbOZdGTHmG3JMApvYX20ETFfWUFgxUdTMlPWmgHezWg+Ypmtwd9/y8/z4gjhr3NYjQP1AUgCFaWwsPLFdUAbcfOsA2UyVWt0mmQxhmopf/eIkc7NF9l/fy56E4t1p+GTOZaYM928iiNy8RHDpNzfqsmsmCpr352BnVHF7r2obcStClubFK5pndio6uwM8+92DTEzkmJjMsmNHklyuSq1mE474CAa9eL0GE+NZPvtomm99/8Aa7gPi3JGtKwLzBQGmKxCs62pmpgrMzhQAxUcfjHP7XSN0dQc3dU4xUHxjh2oOldeulV3VgZCiOyAb3DvXsIRSKG7vFTeNmiNUqo1cFaI+cZh4bGjr0w3LENeCNyeFc79W97Nsy3n25LbNQWnLO3m2LJOn/QmZbtTsZqKjgsmSjPU3E49dysu+4Rvbl7/urjika4rZqTyuhmymSjDkkSCafCulRKF10yLR46PHN8+uzgj13hANV7j1n8zL2r49CkMdAWIBi8/HcgBUG1vZknxdrfoanK4ohSbQJC3vjktn680piRtdz7eyVSebQpXkFojo+TqcuVpk+uoCDzywnWKhhm277NrTSaPhcuzdqyzMl/jw/XEOHx3l5RfP4rgaT5Ns42+aU3u8Brm5CqdPzbF9RwdvvX6JesMhm1kd+xOL+Tl3pk5HMsjJEzMMDyewbRc/EDAVflMAakFDZrrMHR4lSt3p1dzZExMwMZ7jvXeu8tjhPZTLkjEdb4743nv7Kj6vxfCurVs4bXrMGoALHo9JKOjFakO87aP/aq1OzRGlutfU+E3E8mtFnGGHTzoq78+63LqG7RHAzd0G1yVFBHU67ZKpsaiIX6vao1/rdYdiQ/OhA31RsdeKJANsi0h832BE6AW39ch/C1XNry9rLEPG83viajF5BfnoMtJWkrzy7rTmqe1qMXL37WkBtGezcH0TQLeoxHVXszOuOD8m/OldcenetsQj7fWtnYp/PKc5Nqu5o0dxYye8Pycq9VbNVcRbcXkpdjVHuX1BsbF5ZHhptFdrmndvj8h41dXSTd0K/+2VcZfeIBvG7m5WXlNxqBvemRHD8tbn9ppi6dTuzNCqgEcRaQKcyGo95Kq6lBP7o60A01bd2Kl4a0rTfQ0AYGVtBEzvWgeYSrqX/PtWDN+hOdpPBPnZj4/j8RgEQ16+KE0tiqICAYve3gj1is3tPQYn0mJNdK2m/CCbrFMZuJKXzhRKrmmfKSl+jqvoDck53ppmdftlkzFZ1PSHhd/aKr8Fleb1MD9XJhD00D8QIZOuMjdbxDQV0aiPYMhLPlfjrTcuUas7PPzobjxrpAROl+BqEQ52mZw8VVgFTJeOGczPl8mmy+ze08WLz53mW88eIBT1bH/A22UAAQAASURBVKmDut7mWaMX+eHtAPVyXlwh1ioD6MAlnSmgchXGqxvbj0U8cs2dy2j2buFcBhnVn89qDnbKxmetClmK6zrk7zNlzXRJRLWGku8vVxeHmYoNYUvRExL+atIvvqWO285/1zw8CL8ZE974euVq0TfUbVbQueT4DYQNPkqV8fkscrkKx7+Y5vGje3n5xbOLzSEAn9/Do0/sJxqexmO41JuP3xaV6dpsRRoI2XKD4xN5FC4eU9G9lQXk61qsr8Fps0xD0Rv1MpOvky7WOTmZZ39/hJmyAMmNPBldrXl3BlJlWSTXMkp3ESP6pF9I7l5Tsa07wOUvqovZ2J3dIfoGoxRyNXI5AZeplADUp765H8M0KBaqy0BitRk1Wq877NiZpFRuEAh4VllA+f0WwZCHRsOhqyvE+XPz7N3XvSgkUkpARMzbFH0ZJv5oEI9/OXdWaU2t4rDPqDE5Ix2Bl54/QzDiIxL2cu7cPKVCDaUglSqyfU8H9mamn1v5foDaQplf/fIk5bKNx1L4vCY+n7WYAuUPmDx+dJTnfnmSet0mbClMwwTL5P7Do2suwHf1wfms4veTmts7NfMVuDTrkqq71BxRsVebTgyzZXAc+f7WA6i1tu8DhHhvGDBTcHjphdM8/q0DXC4YRL1iczMaF4P8Fuh9Zoci7lOcyWg+SYm62WdparZQCRxX/FBrDtzTv3xM2xppGcrlH85q7uwV31SQ1/qrfQZ/MSoG1b8d01zIijPC/o7ln8FnKn64T0b1t3RJBOpn89Cu27uc11y/XV6v0hzx1xy5If3sooglugPLOWefLUhn43IBJkqygO+Iii9qe5Vs4QcOt42W7+qTjPg/1IUl5hNXhL8/2/J5hZs6Fb8Z+0PPUZma5Osaj7l1YArCDa46mmMzcNcmKWBr1R8KTLfy2S7lZSQc9ykWFko8+/0D/OQfP18migoELJ793kHGrqbp7ArTPRDh+g6DasNlrCCTh63WfEXzzoxmJKw4uk2t2EBJZ3qhJjnpHkMSgm7qXLoe9sTFk/joyBLwTvgUjw4rKvkGn38yxY7dnfT0hrBMg3SpjmUZ+P0epqeLpOfL3HpoiGQyyNUrGUIhD/GuIGi9qCHoC0lufb3SILLJuDYc9pLPVSnka3i9FlOTebb5Epg+AxDD+K1aOLVKodgRg4u5pa5/uqb5NKV5cvvq52rfOGfLNoaScf16SXqzZdnIPjq0sehvZTkabu5SG/rCfjirSVVlyhLzyufwGEJX+3xeBMe396hFsFmoay4X4KM53bRME47taAKCJvzkgghS1yqNdDU/ndfsiUkq30RBHruywlEftZpNLBZgbnaCY+9e5bEje8T6MVeloyNAOOzFNA3srv3U1mi89DSP1fsXCyjtoIBkyMPFuTINR9NwXOzmPVc0EoqG6zKY8DOSDPxR+ef/nutrcNpWAwk/+apNue6SLtZZKNYZiXj5NLV+R0Mj/ojXJxX39BlMlzWOqzkxmadSd4j4Lfb3RzCU4uiIXPAJv1x0jUpkWQJLNl9jer7CfXcM8ec/vJlMukyhUCOZDBGN+dG4jGyLY3kNHK3RrajRJhA6eWKGw0dH+f2rF+joCJBOV9BNC6jHjozy6ccTPPu9g7z1xiXQ8Nmnkzz1jet44blTcp/S0MqmfvzwKCeKBneFl7izS4vbaUKdEXYPR3FcjUJTKtQoF6V7IrxGNl2wt1oGkM/V+fE/fEal0sDW0j0sG4pozM9LL5zhO98/QKFQ5/1jY9xz3w6KBQH4sYSfbTs7OVnzEqxp4j4R7+RrsunwWxAwZdT7/BVNrKHZP6S4rU9SoCZLzXMjJDeRE2nNK+OagZB4XbZ3nJRSTE8XKFRtvIZwPAv1ZrIWMFtwmJ4q0huKMVuS1wxa8Pjw6lHx3oRib2JppKcQP1FXwyPDq7ud7XV7j0FPQIQ7+ztEaDVfXVpEQ5bimR2K56+4zJVZBU7lmMsJ8ctLYpW2Ny6enyATgpHIUueiNe7/6QXNN7aJQ8CptObbu5a/x7mydHb3dSjuWMGxPJ0RJXXUK9/tvhVdzJCl6AqIuOXL1kRJM1WU9BnHhRevuBwe+WPcCDQfp+R9244kHu28BvufhivHxW+JTdVKsL5RbQ5MhW4U9qhrBqa2K53LqwWxJesLgkbh2C4nv5jhB395C6lUifRCmY5kkM6uEG+/cYnhkQRzc0X6hqLYjmY0ITzpbVukb2jkGjs8otbpqit2xBQ7NngOjyECL2/bZsYAInaDmZkiXr9FqVDjYqnKYH+M7p4w2WyVqak8sbifvfu7qJTr/Oynx7nnvu2kUiWKhTqpVIlkZ5Ce3giBkEW51ODYu1e5/oa+ZfSr9vL7Lbp7Inz4/jhKCU++BVQ7ekJcyDjMV8Wh41prICQbxxY4PTYjNndrJa/Vqg6/efEM9YYkXbXoG2sl6RXqmg/m5BpeCZoXqnJc1+MQ+0xFf2jj931TF1jG6p1mh0+sqequdFM/mHWxXQGuO2OKfYklOp0EWEigQ9K/Nt+07si51BVQPDYk/P+3pzQ9HbDyGnBcTXdPGNt2CIY8+HwWc7NFnvvlKaJRH6Gwl5npPK7r8uDDu6mVbawNRgKtRroGZvJ1ZC67RDNZ/qfB1YUqPsugP/61ST98DU7xer1s374dAJ9SHNoe54PLWcp1zVS2SmfEx9Ft6z/+fFYUmK2bqV83eOd8vglIXNJFl7fOLRD2W4z2hhmKLB3ylRGEHkORy1R46XcX+c4z17FtewfH3r3KpYvpRVFS+y5XqaU0C5C/nzo5y8OP7qZWs6nXHcIRL9GIj2rV5vGje3n7jUucOZ0iEPTw4MO7OXdpgSNP7adYsZlNlQnGAmwbjJCImJwYd7mYg50xANXWFXQx82U6u/oXrbAcx8UyDVBNQYxfxujOOqOuzardK7BRdbh4foFKVUw7xTZUYzuaXLaCYSgmx/Mce+8q+VyV2enCosvApckC730yw9PPHuDjlEHD1RTqYnkUMGVENNnMmg8lLM7luhmIGouj9IFli6x0w/YnNGMFeHVc8/R2+XdopotkKtQdIeybSsbWQFMQBrpc4dHRBGi3eXPYGCS0d72uWwNErlfbooofhOG/nxXz94otALAd9DkaOjbgaD29XfHry/L9zVWaNji2pL78cK+A25YC+y9G4ZOU5p8vwZ+NKn50TpOuLo9j3ROH02m4uW2ykK25nMrATEm8CTdSsx7sVFzOa7I1l7hv8xbqeFHSlzyG3DCLDbk17EuIfcxsRbpOLQ/Y9ihJy7Lo6dkKWhBg6jWEi/zyGNcMTF+fFA5uV0DOqZ7A1gIEtgJMP0kJbUHrjYFpw9V8NLdEA1moimhkZ0zxxMhSR7KVEDc5meM//2/v0z8QpSMZ5ML5eaYm8/j9Fnv392CYBs2GOhGvaoZWbKXEWeHW7vWA6dYrYLLsfGrUXDILZVKpIjNTBQaH4qRmiyilmJstoADLY1Iu1snnanxwbIxSqU5XT5iFVInnfn2KYqFONOrDMBRPf3O/0JmyVfx+iyNPLndeAQGmh58YxWNJLOb1N/QyMZ4j0RGgWKjh8RjEDYuBLpPZima2JJvagLV6g7ZWuVpsi9xmd1ApVvmiGs3PPn4lSzZTxbIUlmEuc2hZ6WbySUqut7W6uZ+kNHdtKjxaXitT79a6zjM1zXszslFTQH9YMRhWdPg0nYHV13tfSBHzaX5+EZ7ZuZpCYLual8cFrPcGFafSsnbd268YWEPwp7UmHPHxwEO7+PyzSR5v0ulqNZtCoSbe4yEPjx0Z5aMPxnF1PyPr0NYcVzqk0tIwCHhN+uJ+vJZBpe4Q8lm4WuMxDU5O5tFN3/GI/ysPyRbrK38k1sotHkkGOD1dIldu8OHlDB0hL8Wajcc0sB2XUs1hsCPAUEeAbdGlOMlUocaZqQJGcziiAKVExV6uNjgxkef2nUsIY60IwmA0gIqF8QYN/vlHny/zH4Xlu1xv0FpKs2hWaq7Er39xkp6eMI6rufeB7QSCHoJBL1OTebp7Iuzb30NXT5gvPpvk/KUMnT1RVHeCPYNxfjfuks5pem3NbT0GH8xB3C856bOzReoNBxRks3WqlcYyKyytZVTh81scObqXcMR7zXzTlaEAXd0hTNMgky4v/o5ju4sJpS5Qq9rMzRVpNFwyWVlEarammq6hm7xGN1ukKxJjtqx5dpcsdOmqRGkmfHB9UjFVVkyW9Zp4sWJrfnReEkX2d4hq/mRabIpaYMTVkOwIEPEoVmZmt9biRCIgHn36D7vxOq7mXE46JXsTAtr2JRS9QUmHGu1ocnSB7+0W7t+xGU1/UEbb6ZqAxyMj679G0q/47m748XkxwP7tmAgT9sQVVUeU9+kadAc0pqG4rUesVCaKAnzbuyd1V/PeDBweXrrh1V3NC1dE1fvMzs1tZ4KWYiSy0dhb89a0eCxWbbku7+1XeJtm4V/Mw7mseK/ujivOZzXvzwq94IGBla+/le+nHZgqFqruoq/sVqoFTA92qkWB1l198PqE5si2jbvjWwWmliGcx82A6evNLntXQH43aKk1VdyuBttxF71OpybzTE3mgSWv01MnZ3jw0T389orD3sT6iT9r1XhRPGptLV37miNA7entq8HHelVoaE4sLEUIKyUBD/MLRao1h0jEx/lz8zx+dJRKucGbb1zm5lsGeOn5M5iWQbkkyuxA0MP3/+wmPv9skr2jPRRyNRzHJb1QJhrz8+pvz7Nzdyf5bJXUXJHOziBHntxLIV9btB8KR7x0dob4/asXJKQg7MVxXDqSQcqlOtWqzYnjE9xwoA8VC9IREHDW5V967+sZ+oNs/lJVzYtXxNbqwRX+qa1p14fvj+HzWZTLddCglSIR92O1qd9abiZTZU3V0QyHVwPCii2Th5Ala1yh3vT33mBTqRHbuMeGN/7+Ej61zKpuqdZ/3O8nNPf1r90pfmNKsysqrii5mlzr39u9saDR6zdIdAQ4eGM/lmXypz+4iVyuSrFYp6MjgGEoTMNgPlUmNVtk12iSur1a2mYo8HlM6g3oCHsZ7Ys0Abm4qnxjx9L1FQ14yJVq10jm+Y9fX3lw2mg0SKVSdHV14fFIm6cn6iNbtpnO1ajVXabr7aMaWSAup0ooBb1RHxGPxBuenS6icIn4TXZ1B4kFPDQcl4VigzMzJeq2y2SmwkBiCVCujCA8kxF7lyvjOaYLNh5DFqBlOe5tu9ye3iVqwKLPqRJlfr3uUC41KJdyvPKbc3R1h+hIBrl8eYFSsY7XZxHyWWwfjOAJyOfyGJo7eg2oOyxMFuhNV6g2Atj9UVIzRaoVG9NUFHI1XvvdBe57cAePPzFKqVCnUKyRSAQIR30ku0NYXmNLhP9WrRQTAXR2hejqChGLB0CD01LEaDBMheNoXK0Jh32USnUBgVpTyleJxANUXUkauTJTIR5P8MiwmCr/fkJGxNd3wI1dsghnyg1idh5TxxGW61L5LfjhXsXn8/DqBJiIf+j5rLwZrwkxr172faysVnzttQL2kq2ZK4vg42pBXASuFDSdfsXDg4qpMjy1XXE6LR6ak0U4k2WZwGd/QjrFP7+k+bNRoRP4LAGc7elO7aXRixnhu+NimXOnJbxDgMMj0uk7MqKa3UdZfFvd8vnqkin6q+NiVdbXxkNLV0UE8c3hzYFpq/J1lolcWnW1IM9/ew/ka4pkYPk14zHEHPxcVqgD57PyHl1XKAsrvREdxyGXyxGLxTDN1Yiz7ooXbNgjwBTAdiVVbK6iF8HmerUWMAW56W+LSod5vU75tQBTxdaAafv78G7QlBZBVIAP3r0qXqeX0mQzbV6nn0zw0CO7iUY8PNIBb04C6E2jJ0GO6Uezmt0JERsFrGbXF5rpahs/vmILR1Ih1JGKrUjnHRZmCwT9Ftn5Il6vSXdPBJ/P4qP3xzh48wAAn3w8yTe/df0iTSHZGWRoOM7E1TTXX9/Ha69ewG6zq3AdLR6vUT/TMwVqNYdTJ8a5895tBAIe/EEJ/4hG/bz9xiWuXM5w5Mm9fPLJJE998zoK+RrJziDP/eoUDz+ym1/9/ATPfu9G+hNebNZO7ltLuDRbgZgHHhhc+wC3pl0er8nu3RFZQ7WsX/lclURHYLFBE08ESFc1b07q5mZgeV3Oi9jPMuDlMbeZdQ+HuhWedbQ+wpvXlBri6bsz+uWSq9aqmbJw8tfa/NiuJulTi8l04yW4q3dzpw3Z0GjiiSBTE1kRbcYDBIMePnx/nNF93RQKNcqlOuGIF1z5fC0P5xZHVinFjcMxGo5L2Ne+YClCHvH9fbwpZIsHPWRLdUDz8dU8BwbDdIS+Fk995cGp67pUq9XFOM9WjfaGGE76yZQazORqlOsu0YBFImgxlq7ScFwuz5W4Ol8m4DUpVm0MhOB9cCi6OLbwmAa9MR+ZcoPZfJ2J9HJw2qoWr9PVmojXoFCokPSJUKJssyqrubXLXaQGvHiGYqGGqzWWYWCaBo8d2SVep8BjR0Z59eVzTE+LgCEU9OKNmBw+unfZgvfAoMHUdJn3Xj2DU5d/tW0Xj8fkrntHZHTd5LLOzZV487VL3Hb7EH6/SSgSJhYP4I2FcHzre/lVbM1YcckmqVUrxUQgpv8+n4XPb+H1mpQrza60aoa3AAG/RWdXkEKuimoed601dsMh7LfI1TTxRIAbOqRj2XDFEy/hWwKmGs25jMvtkUYz3WY5IFGIKj5gaR4dVMQW+ZYihLEMUcm6GNz+6Ci/ef4MHu0sdgmFjjHKhzmDvoZme1RRb1IM4l5ZFD9fWKICnM+KKKjmyKLX4RO/xn0JEQ08mVxaaFueq8JZk3+br2ieu7IEgg2l2BkTUcDn85Iz/u2dIpRaqy7kNJ/MaZIBGbevNWbuDcr7m6/K8V5K4ln6eUvsMVsWcL/88cIL/N342jfDlVWytcQkrvAl02g+n291/tS6djJVGx4abBnuC8B7f1ZzJgsl2+X2niUvzrcnHQrZGoMNh4M9BpMluJqHXF1oAjUHDiTVshtjzCcUn89Smhu7WBegrgdMW7W/Q2IrXVZ3T/+lgelmpdH4Aib7b+jjnbcus29/D+Gwj4X5EoapOPLEPgIBa3Fjet+A5vfjmtomu9SKrfnNmDhKTBY1tgv3DYjN2gdzspl8cptJNGiKgMS2qdaXntRufpawBQ8NW1gGxKslXvznMzQaDkef3IvPJ+JPj0dx5IlRXnrhLL6Axa2HhujpDTM7I6P9/df3EAx6KJZqDAwlAJfLl9LL3q/juFSqDuGID7/P4tOPJ7jplkH+/r9+zPf+7CDDIz1MTeYZu5Lhuht6ufPubeRyFZ58ah+WpfB4DGZnCxhKsbBQJhYLcOH8PNdd30MwbDE3U161Fq4ULrlaczqj143zVGpp2lVvyHu1vBa6boOSx7cSBr0ek1hXhN9NuDw+vDpytGrrxbS7+wdgq13sm7uk412zXfINSbcrz7nc06cI/4G+tyfTepX9W6ssQ3FjG32oyw8fzgn/eaPSWhMIennp+dPcdPMglYrN2FgWn9di92gXn3w0wU23DFCvO3T3RJhLV7lY9ZDwL92PWuWzDHwrdmWZmtzPH+hf6jYPdQTwWgZnp2XqminbX4NTvganG1bAYxKIm6sIyn1xP7O5GhOZKpWGQ7nqLApI9vSE1uTTdIQ8zObr1DdRrn+a0jyzQ2M1x/WWAfXVHHsi8QDvzWjmKy69wQBHv3WAmatpZqYKRGM+BgZjvPDr04tep6I6HKVaEdXh4HCMoeH4qp142HF46dUzOHWHVtZwPidZ9QqFx2Mu5ta7jktqrsSLz58h2RXiscf3YCjoilubdgdrDrw7o+nww8EkBD3Gkrir7fCVipKE9cnH4zx+dJQXnz+zLO41EBC1fqkoUbD1RnMx19LdAPD7TbYNLXUsPYai1NDLkk6Oz8uo17NJU3Ol3ZBlqGUcSgCDIH/5g4NcHC9QK1To7VzqetzmigfhJym5AXf4xQmg3JAuhKvFr6/DL2rUqsMqtXLHFvjynQHFgwOa1yZZtIQCuLNXggcMYG/HEt9yZcW90rUaDKtF7uDKUigeGhSR31hBgPXeuOJM86ahUPz6istCRQD3WqLC7+1W/N0ZTc3Rm2ZwvzOluXMNs/pPUsKN3LUB13OiKIkwS/YzS9YvxYZ0iP/5omZ3HIKWxtaaW7oVGUNCEtIVGS2HvBLxaqnVvDm/KbGgu2PrA9StAEKFRK/+flxzqGcpevZfGpg2XDkn/aa4R2TrwlXuCQg4SdcVYwWxLkp2B3j86CgzUwWq1Toj2+LEEwECQc+yiYmhFNujmtIGMZyTJc1bU5o9cbGA0lrELRGPIuKBh4cUumxz5UyK+VSJZFeQwcEY/oCF32PQULBQg11xxbBlc+FUiljcz4vPnaFetwmFvMxNC8f0nbcuEYl46ewKcfiJUapVm88+nmb7zji33DaAUhJCMjNTJB73owzNP/4fx1cBD9M0cKs22WyFRw/v4fXfX+TzT6c4/ORepqcKKAXdPRH8AQ/pdJmG4zCyLYFlKRoNl0SHn6uXMxQKNTIZoQnkc1Xm5op06dBi2p0y1KJWdblwyeDVcQmkuG4NVwgDcOouc9MFyqUGlmXw+RfTHHlilN++tGSN5Ngab8TkkSOjfJo3eGhweXZ9zRE6Tr4mwHQjBf5G5bMMuizZaGdr8PqU5vDw2tzTa6nkFrjZAD1BmWo4rl4jXW6plFLkchVuvGmQt9+8DFoeUyrWqdcdHjsyyicfTXD4iVGCIYupyQJ3701uSVsxWRIf6AcGlvOpM6U64wvlxas0fC3coP/A9TU4/RJlGYqBhJ/+uI9M2aZcd6jZLmGfSU907bv94kWoNdlyg3jQs8jRXCrJbveZLI6Hqdurxller0koGSGh5eZUc6HuKgJBDwsLJc6cnuW++3eQy1YX70tzs0V+/8p54nFJTrrplgFZ/NqeVzXV/416MxdbQaPuLILRs6fneOTxPfzsJ19QrdloZKfpD3i4555tfPTBODt2djAwEls3GQvEOudAUgj3EyXFS2Mud/YtdWQbdQfb1s0Og8nJEzPccusQxz+f4vHDeyiVG+RzVTqSQYZH4rz91mWy6QqHn9jLyy+dpVq1cTRYlqKmDP7kG3uJhpZAuKvFKLndE297FAKGYmFhs29fynY1F/NyeD0GDIWX+5EGQyY37I3z3NU4t21Ti/G1HkPxvd0CQIOeZvRnk0/maonBc1zxY9S61X38cgv4tqjB94LClU36YGdcgMb39yj+qSlYqjtif7Sy+9IZUDw23ORoFjVXPPIZT2fEC/fGTkXUJ/GsxYZ0Ru/okfErCuYrshiXG/BX+9af6RpKAgauFlbncFdsAUsRr/BDkwG1aiR4KaeZLq1PTVgsBb+6pNdMogp7FGEP/KAp6poqwWP9ikwG9iQUQzFJ9vp0XnNfv8FG8dijCclOf3BQ8fsJTV9IhFIKsfB6Z0pzY9fmncp7++Vm+s605q5eAYr/ksB0tiJikZAlQCjqUfgscWK4nJcUr30dSxulFh1peFcC0xD+gePqVTHNIFOO8Doj+TMZlw/nZGNmKcVQWNjarcmEBaSmS/zT339KpdJYfJzfb/H9P7+JaFzM8p1snZit+d9/9Dk9vWEO3tTP9FSeZGeIRsNlbq5IRzLIoTtGeO+dKxy6Y4jBoRgL82W06+L1ejh5Yg7LUnR1Rejo8DMxnm9uzsUar/3zGE0LJoB3377CM9++nly2SqlYp7NbaEiuqzFNg77eiHQqXY3WCo9HMTGew7QMKuUGiUSQibEc23d0kJot4feaVCo2pWKNaMy/aDlkWcYipcvpiuOi1wWmC3MV3nr9EiPbEpSKNTQsRlsfeWLvopvJ4FCMxECc8xWD/YnlwNR2xY5rV0zxwMDWPdzOZDT9ofW5qHGfBCI0VoikrqVKTe7rRhaP7VWzm/Gkm3wMQ8H0dJEz59N867sHmJnKM58qE4n6CEd85LIVbrltkM7OENqVe7Fe3D60ajkvfrKkOZ+VVMjDI2pZ2lal7nCi6YWqgIh/fQzxVauvwekfUEopOkIeOkKbkKGAZNhLLGCRqzh8MZ4j4DWp2y67e8J0t52Mpeb6u1LJ36rF0U7MpJPlPM177t/BfKpENlvl2HtjHH5ilN+8ILtkw1AoQ1Eo1jl05wi/+OfjFAt1nv3Tg/T2hXBgcWTfXnZb22xiMsf+63t4/OgopWKdXK5KIhEgGvMvxoTGb12ewrRWreRT3ZoIENIxKhE/mXRlEQyDgBfH0Zw4PsOjh/dy/tw8juMyPBKnqyfC878+JYIMpTh5YoZHjkgXNZ+vMrKtg6HB1TytiSL0rUjjiXgV9gbdHWBRTZupaVLVpsVVQzpNb05ByKM5PLQ0rio0NDVbo/XyFdFrKHTNYXx8NZ+sRd/YousOsGTGPVWSzoRHKMMYCu4fkHH+29Oat6fl901Dfue6pOLkgtix3NHbiuNdOi5BSwz4QfwFT2dEDBX3waUcTJQEUPYGxQXhlTFN1Wl6uyr43bhYzmyUSOUigrSVG7CKrXlzSvht13WIP+c3dy5/jpItdjffWkOpu7JytfV/JuNR4esOhBRHRkA7S88X8Sqe3KZ4ecylUNcbxnD2BBQ0aQUPD4rNj0LU7+/PSkzpVro9IY8i5FH0BDUfpTQLZTg6ImlEf0xg2nBFFJiuilhtbWGL4rYVN9xW6aZrxkbV0CJQXFmZqsulnHj7tjaKDRdeGtNMlTR39Sq8dWcVMAWJY/7RP3zKX//t7dQ1eJTi7//hE+p1h1sODXHpwgKWZWDbLvWmb+Ubr13k6Wf289Aju5idKzKfKjM4HCcS8TE3WyAY8JBIBvj0owkuX85yw4G+ZpesGZtcbqAURGN+bNvlsSOjHHv3KoUm0LMsk84uLwMDUQqFWpMKJJt8yzIXHVRqNZtazeH998YIBD0kk0FyuYpQl3wmM7NFXMfFdWVqFYn6cR2XbKFGNOpjPl2hFIhzf//q7yNEhWzFu/h+d492YXnktV2tuXBugUy6QjTmJxjx4u+Nk3IMrm+zxSvZmos5uFIQpftmWfUrq+7Cy2OasAceGVpPLKU4nWHV1GmrdTaj2RaR59msFqqyljw2tDm33dEaggEK2Qpvv3mJu+8ZobMrRCZTIZMuEwx5icf9WKbi/feucsPBfiYuLtDVG8UXMElVNe/OyFROo/lwViYeh3oUI5HVyNhrGYtX1q7uIIOJr22kWvWVB6eWZdHZ2Yll/csfih1dAT4bL2BoTa3Jlzo7XeDMdIG9fRG6oz7u6ZPfXUvJ326+3wJb7TzNs6dneeLp/fz0nz5nbrbIe+9c5fEje6hUbVxHY3kMBgaivPn6JeZmi2jgJ//4Gf/T395OIGzhalap/602AnkhXyMc9omq1WMQCnqZnclTb7igtyb4WUv0BHDvA9vRrli51Nt4DG6TVuAPWPgCJtt3dHDs3SuMj2Xxek1uPTTEyy+dw++3GJurcOV3l+hMBnjo0d1kPT4M3+pOTjIgeea2u3ysZBgm0WgUw1jeGmsBi6AlBs6pqsQEBiyx2VqoyE31cl7zzxfFXqrDr7iSZ1VnY73P384nK9rSQW+pOR1XM16Eoq2xHcUXaY2lBISErKXx+4ODkK+J0GmmJIDr5TEBb3f1CpD2GtIpnCyJzVLFgVpV8+vL8hn6gsKb9JoC6IRjKdGlnX7F5/OaPXFRU9/ZK0ridFUzW5Fs6SGfYjQh761ma06lhaoStOT5w17JCj+fFXcBhQDa+apQPFqjfdsVVf1Dg2K+/8zOlUbs8PxlzY2d6wGq5eXope7dWEFec09cHvfujNAbjo4sKWjdtnOhZbNkqY3zwleWZShu7Zbz56M5zSNDa1vzbFQBS3FXj8Tb/uoyGEozGOGPBkxfn5Tu2y1dqx9jGAamIZ38lZz8a6nuAJxMr34/sxXFYHj5BMNjKHoDcm2eyhp0LqRXAdNWVSo201N5ojE/obBnMXq0kKsSjfrxeE1s26VYqBEMeViYL/Ph++McumOEvr4IlXKDTKZCqSRe0uVSjV/97CQXzsv4pFF3OPLkPhoNm2e/f5Bisda0YjLweEyOvSvWdU88tY9EIsBLz59lz95OolEvgYCF44iwrlKxaTTqKDQ+n4diocabr19CKTjy5F6Ofz7Fvfft4IMPxnjmT27gzJk5HEdjGAaNhghdi8U6obCXdLpCIhbgYM9qpxmvXaJ84gXmoo9I7Gauyofvj3H46Ci/efEstaoA1FrNoV6zufX+XcRCJv2L57QEgnwwK64kR4c3tnZbrw4kJZb5Sl7zT+c1fzEKK8+tqFfWgy9bFVutS0lqlaMlkGChCg8PqnW5uctKw75tEU4dM5kYy/HhBxPccusQCgiFvFgeA4XinbevcOPNA7z2uwsUMkX8QT+PPLGPU3aAhwZF3Pd5Sv58dtf6m+ea7S7+ZCO6wVexvvLg1DTlJvSvUbGAh9u2xchVbGzHZTZfp1iTBImz0wVShRr7+yOLv79Syb84/m3+vDWGb4Gc8Ykce/b1cPjJvZSau/la3aajI8DxL2ZIzRbxeUe4eGFhcQhRLNYZu5qhfyBGJO5drjbXEhUqo2eNzy8m048e3sNvXjxLqVBcVHt6fSZHntiLP2jJ7nOdWkv0FApLXODnn00vesu1A1SPz+Thx3bzQdqg7nq54Z5dNDIF8tkK4aiPH/zNIeZmi6QzFWLxANuHIgSCJrmM5lJBOIDtFbIUT25bzYE0DINAQOTldUfz4ZxwrhwtXM2ET0RMo4nlICPikb3v7rjiUt7lzSlRYppKOHubfX5Yzif7KGMwVdLYrl62gG+PQswLT47IyNVjCNidLktCjmJJDBSJK3bHNf9wVpSkYY+io20xHwgpBlbxx6SDVrJhrABDYfGbrDngt6Tr0BMUsHUqDamKfM+WITeam7rUYjqK1xBrm/sGYLwgHFvLkK7K3rjitm7FlQJka4odUREZvT6peWBAAGrEq9iX0Lw9JWC41dWpu5pUWcBu2W563m6hDiTh4zn4r6eXDuh8RbpEHiVpVO3WSe3nwtmMJuKlmVV+bTeQki3ejQ8OXjswBdmcvTGluXdAkfDCK+MS6Rj1aHI1uJRXfxAwPdC59J21ygTKJZvJ8RwL8yWSnSEGhmIEQ9amefBrleOyapoyX9FcymsOr2EvdKhHMVmCnrDBx2c2Tl1YmC8TifoJ+EVAEgp5JQCgP4rXY4CSbtX7743xyON7+PD9MTLpsxy4UUzzfR5FIOClXK5heSx27u4knggu2kAppUnNFUh0hJiezLFjdxd2zWF2tsAddw3T2xfF6zV57+0rzKdK3HLbANlMlc6uDoqFOn6fiWWCN2FhN1ympwukFyrc/9AuerrDTdFVnA/eH+PRx/Zw6tQM4bCfQDPXHZYEqa4ja3As5sPvM3BkicZQzVjf9GVKx18mM3qIRkPoWKlUiQ+OjS3SoXK5Kv39UfoGY5xq+PjoqkvY01yrHOFw3tGzmj7zZWpbVHE+J/ZzraS6Vu3vgBeuakbjS6LYbE26rZsB4ootCvmb14mcBpluvT6hMU14etu1xcr526aW588ucPVylvse2IHHY1Aq1cmky9x+5wjvvHWZyxfTGIYiqk2e//kJnv7WDZSKHk6XDUylmxHP67/P4xP5pc+1hsPLV7m+8uDUcRxKpRKhUGhNy5g/dgW9JsEmV2moI0Cl4TCZqTKRqZEu1nnr3ALxoAe/x2RbZ1Da/k0lf6s+nJWkom0xA+9MhYWaXrxJf/DhODfe0Mvx4zN4LIPZmTzpdBW74fD40VHee+fqErm/+ZiF+TKZTIUbbh7Au4JOoJSkMFWqDR57fA/H3ruKxzJ44ql9KENRLFSJxQLE4n7m5ooUi7Vl3V3bFWujTE2zI26QnipQbHJCPYYsioGAh1y2wnyqyPHPpnjsCQHXhXyVeEw4sqVCjXuui2K7mnemDbJGjER/nFNFTWlK0x2MccfBOJ2BJdXkRibWa4lzXNelVqvi8/nxmBLr6TWXe+gphFN1MadRShTgZRuSfk1PUNTiO6Ky8PYF2x+1ejOxslp8svt3LBk7u1pvGmc3FF7vJ4o9cUluur0H9m8ap6no8EMHsgk6nxPg2S6CsAyJFxwMswUeml70PfUZiporIPSTlKbQEK5ttqbpDChKDQFQv76sOdQtHcr9HZLQpZHIQkOJ2GqyJN3rfQkRsbXqUk5sogSgr/yswqH9JKUJmjBWhLNNO6nugFiFtVf7ubBQlW7QxsB09di71KQm3D+wtg/jZuVqAZB7EorB5nfwwIB4yUa88MtLsC8hne2tANMDya0B05mpEj/9p88XQy8AAn4Pz37/IL39oWsGqJ0BRX7WXXxvJVvz7qy4Xqx9bkssqNfUdHZtHDWU7AyiFNSqDbSGUqlOLBbg/ffGePCR3XxwbIxctsLcbJFj717l4Ud3obXQlzwek2jcT6lcX+y0JhIBvB6TcMRHZ1eIDz8Y46P3J+jqDnHPfdtp1ByKxSpDTTpAJlPh2HtXyWWrmJaisyuE1uJQgQKUomFrCvMVPJZiZCTOzp0doGRtnpzIEw57eeZbN1CvN/jw/Ql6esM88eQ+fvQPn2HbLoahMAyF5TF47MgeJsezJDuDVCoNJsfzzM8VSXaF6O/qx+oZJeav4dhus5kBM7Mlnn/+jPAmQ176+iPMTOXoHulhOKwW47d3RhW3dK+fhvhlKupV5OurwWnAUmyPCKXo7r4lsdDjw4pcXXMmI6BbNoRL5bjCoT/Us7YtVLoq3d/JkiboYUPtw3rVPrWcmymwkK5gOy7ReBCvzyKfq/Lcr04xO13AMETg1vK+HbuU5sLlDA8/tGNVHOxaFfAYiyLe8XSVdKlBIuhhe9fXMaZfeXBq2zbz8/P4fL5/FXC6sgIek13dIYJekwtzZVwN+XKNPAoNjPauRh5xn4imbkhAtRbgvG/J07GYqfDF59Pcfc828rkqALG4n1rN4dh7VxfV+8Aih7sjGeTkyVk6e6Pk43FKjp+nvnOA3FyBVKqMx2PQPxBjfq5IV1eYeCJAd0+YeMJHNlPjd789TyazxFVtH1E7GuaaPneX81CZrzQXDEXdlc6kztUZGJT2ZmquxLG3L+MJeDC9Hi5OFNC1Oo8/vrsZi6oIWi5jeVBaE/dCwgvDUVG3X6OF6LJyXYd8Pk8y6cEyPItjI9mpC8A+m5FR/tERtczSyHFltP3ujCbmhYNJxScpzbfbeJJrcXpXVssirLWo/qEL1G09CsMQa6jNwelSDUcUA2HpZmqk0zpfEVX1cFToA9MlEfyAIl3VhL3SvRP+qOLDWTiRhu/uls7nQlU2I1Gv8FXREvXZbNJzU5ciU9VczGsuz0LEK76mJ5v+rYe6hSbxcUoSqlYem56QjPpv71FrRmUudYulQxzyiJCrL7SaF9d+LuyIWnw8Bw+uK77QvHBFlP4tqkALmLZU59daawFTEKeG2RIU6/DkNnh7WjYnmwHTwZDi/VmNz5SR+Q3J1cAUpGO6EpgCVKoNfvqjz/nrvz2Eb6Wv3aaliXjkmIQseG1CjstmnpO1ikP/QJRAwLPmaD8QsOjrj2KaBtVqA3/AszjCz2WrvPPWFb79nRuYny9z5XKaSMRPve4Qi/tQKoDWmmyuSiIeIDVXolCo0dsXYWAwSrlcRynYvr2DcNhHKOQlEvERjfsxVITf/uYcY1ezGEq4/D6fyb33j1KvOfT0R3BdF8MwSM0VCYa8dHeHyOdqTE/mSHSEKJbqNOo2O3d1UK3azM4U+PCDcVxXc8edIxz/YorHjuyhWmlgGAbKgL7+KK//7gI33zrI+fPzaAc++XiCyfEcjqPx+0y+892/oVvP4g9YFEoNzLb9Y6lQw244+LwWPr9JQ7mcyWgeGTIYimhOpzUvj8lm0NFwQ4eiN8QyAc+11q6YWDiJ2HH584xE5FoGxdtTmsEwvDqhSfgUe+Nr+6F+kpI1bS0hmGyGoScooNZvfvn7QWtqObgjwfDOBCjF3EyZC+cXeO/tKziObBocRwPC13ebiYV+j1oVB7te7e2LcGIyT6kqAuNCTVOsVfFaiqGN4vu+AvWVB6f/o1R/3E9vzEep5vDJ1fya8gPDUJzJiur3u7tcTAMavRF8Xot63cG2XfK5KvWawy9/doJkMshDj+2moyPIf/svH66Z+xwIWHR2hrh0foHOZJCbbw1SDXt5fUZxcyLC+OczlIs1PvxgnHBYIkHn5oocPz7NN751gOd+cXLDEbUvYHJbz1LncCIf4IwBjYaNbWtJGqnWCYZ9eL0WZvOG1ag0aFQaaBcc00TFIrw/q8nWNNmqAPS7+uDXl+HxEcUHswIONkrV+bLlIiMvU4nZ/Vo3VdOQPOlv75QM8/dnNabBMpXvWpzelUlS8cTmgrJrqVRFcyWvm52/aytTKR4YEICaqWrOZaUDclefomLDi1eEYmIq8e5rURAOdoq9VldANlGvjmsOjyy3fRleo9tbtjXTZYlKfWr70igfRDj11mQzetYnnemYV0IUWv6qrisbgG2bCjjUoh3XcGTj3wTpHJ/NuM3x5NqdvqPb4DdXNR6l6Q6pfxFg2nqtJ7drfnoB3piCP9mh1ky5g+XAdKKkOToiHMJ8XfPOjKYzwLJjbBgGk+O5VcC0VdKpy7Frf9eWOKiFhliMZWpyLbwxqbkhqQha7ZZea5dSivn5Iqah+P6f37RKFBUIWHzvz27CtMDrhZdfvMh3v3+QH/3DZ3z4/tgiPeiff3Kcbzyzn0O3DzPXjC31eE26u8PkshVyuRrzTomenjA7dnVQLtXJZivUGy6WYdPTGyEQ8Ij/dNSPdjWTM3nuvHsb+6+rkk6XiSeky+WxDCJRH/6ghe24WF6DSMRHNlMl19y8R6J+vF6TgGNRLtXIZquEQz7KlQaje7t5+NEYH384zhefzXDmdIonnt5Hf3+UH/3DZ3g8JjMzRfoHonz4/jiXLy1w6I4RFubLFAs1KlWbn/zkJH/z1zfw1JNd/PjHZ6i02e75/BaHj4xy7myKhx7fzYvTSxM3hWJ/h0yLAAp1zeU8nMpo7u3nS53HINe8xxC1+u748p91BhSzFc0r4y59QdnYda4Iz1hZhYa4Xaxdivv6ZU29nJd1Iu4T14ts0xptrixxzj1BRcwr69lGNILW1NI0YHIyhy8gNolat4Dv0p3acVziiQBTkznqtrssDra96q7mi3np8D4yqLhpWKh+lqH45GoWMJjK1r4Gp//Wb+DrWipDKdKlhjQ0lcG2TpmFtHPAck0OmB2I4Q1ZGM0x/EvPnya9UEYpuUi8XpM779nGa69eoCMZ4DvfP8hPf/wF5VJ98fUCAYtnv3eQd96+DAqiUT+/++15jjy1l6e2W1y9mGFisoDT9N7M1Wv4zRq2K/nPs1M5POukIa3MaoZmp7MjSLnUWHYDNJTi5BfiEfjeO1exGw6OLXZSobCHZ57ai1MtY00VGYwEuKUvQsoxeHNS0om6ArLTfnlM8/jQH3csBcJRHY0DiAJzs7ohKdzMV8dkNC6PlYVuidNrU2+mVyWbHdprSZDK1kQ0tVn3qSsghvtns2LYHW++1lBYxF2biQRaAPWtSb0ITEHEYIdH4Pkrokz1W9KRfHd66bENV15/oihd89ZxWK9eHZfULcVy0ASy6bi9F/75guaJbSKGyNakK1O2Nf0hxY6I+LNu5A6wWZ3NakYiqxfGe/sVv9kgTUsh3NkP5sAyJKrxjw9MpQKWwTe2u7wyDm/PaO7tk3fQXg1X88q4nEcTJc2DA0vilqhXcXevxDve3790rE0DFuY34XgulBk1ZCOwUbVM8Q8mFR0+mSL0BUUc98DA+sclVxPP2Z1xRT5XIxDwMDOT52//T3cwMZ5jfr5EsjPIwECMQMDE1S5/979/ytxskWRnkL/5X25nYiJHvdbgh399K/l8tclBjREKexnZngA0hbwEdozu6yQY9HD65Bz1ukOl3EAr6O4K4fN7cF2XCD7S82VSc0V6eiN0dYeZny3jC1js3t1JsVQjEPQSDFgY5tKG1AWiCR8+v0V6Nk2tWEBHenFcjeVV9PRGyWUrpFIFOpMhojEfqbkSXq+Hhx7dRTDoJRb189rvLnDHXSN88tEE3/neQXw+k2KxTqVsU6vZBINeyqU6btOPc3ymTs/0T/nhf/pPXLiQJpdtRan6OHc2xR13jeD1m9zWo4l5YC1KSsSrONAJQ5FmIERzTbq+Q21h87e87u0Xb+XuAIsWYbDE996fgIE1olJX1lhRN63nYLosz7cyYnesoHl7WiKlP5jVNBzZhI0mVHPCIBvryaJM9E5nhEPfFZDkvYHw6ucEaSwEQl4CXhOv16K6eP9StEaQwaCXQMBDJlvF7/cwM1/B6k3Q4RPtgOPCmYwkW+1PyFr1xpS81x1RD3azK6FwCXqvQXn5H7S+Bqf/A1WxZnN5voLGYCQpqRFb4YB19gR58hv7uXwxTbEkGcCuK8b7c7NF5maLBAIW/+lvbmNyIsfCQplkMkiyM8Tbb13mwrl5fH6LcJNDNTtTYNuuDnKZyjKQ4GjJuga5gOdTskCXinXWqpUj6lrF4c3XLvHYERFUtTq5rtZMTxV45LEQ33r2Bs6dS5FNVyVCcCjGyy+dpVioU27m0Zsekyee3Mu+jgBdzdHP7rgsWs9f1Rz9I5g7r1epChybcYl6FTGvxnGFh+mimCjK8Tm6Tbh9+zs0JxdE+dqqdouwdMGWHTgan1fCBFbaXq1X+brYK5VtAUzrgdTzWegNian5xbwImRJeESa9M63Zl5Du4UaA/nRaY5lLwLRVIY+iJwC/n9TNTGzFnX1LwHp3XERiv7zk0rWFJsDtPXLjWM8T029K9N8Hs5qHBxVxn+LRIeHMXS3AhbzcRN6dEa5r0JLuSMwnCTHLLaA08xXZZAUt6fBaSqzcXrqiuSmpW45QaKQzHDBXc4BbmxXxMVUMhDW3dG0+sl6rtgJMW2WZiqBHHBZ/drFlpyXVAqatY9kOTFu1FkB1XEh2bsLxTAZxtUwK1st7BwFot/cs+ag+4JPO8nBYXrve5B+3d8lKtubNZgqRq0UomU1X8Pu9/PRHX/DQIzu5/c4hTNOkXK6RydRIzRYYHklww4E++f1smW3bE5RKddIL0tXs74syM1OgWrPp6goRjvgol+oce+cqQyMJMukSu/d08fKLZ7n19mE8pnSQOzrDaO3i91v09EUwTcX8XIlCsUZnMoTPZ2FY4i+NgmKhRrnUYDi8pDx0AcuoEPni/0u0XqQR/T+jg71UKw7lchXbdunujhAKe6lUZD3oH4ji91skOoJUKnVuuXWQatXmyJP78HgN8jnp2NqOSyFfw+8zUUq8lFGiIRihSvXY/4dt9/xfqBXqZNNlfH6TRx7fvbjGbHaOgVBfKrbYfa21RujmFbDRxMprKO7sFTu7wyNL4O/jOc09/YqB8ObvY6Eq3cbhMDx3BboCirhXvFRb7+ODWXEB+ZOd4i4yWwbTA4dHlltIBa1WF7c5cdHCfX1zSs7lw8PLKQUGMJFukIgFeO+Nixx+YpQXn5PksRYwDQQ8HH1KkhpNQ1GrNujqCFB3Fe/PgiOZNuyJi+Cv9dq74kuf0TIUpqHQLoS+NuL/GpwahoHf78fYzJ33X6GKVUF+pmksplKVSzY/+afP23ZqUpVqg5/86HP+p789hD/s4eqVDGfPp4jGQ8Tjmud/dXrplxWcO7NAKlXmvgd24Mw6fPrRBBOTebQGb3Pcc/LEDCCgElaPoE2llnLNFSS7gly8sjzWr73aR9QtMdDcbJFGw1lmBN3a1VfKDY69e5F6wyEQ8LBjZwf//e8+oVoVn9Z4IkDZgQAOL71whr/+i4O0x4xuiyoarvDwHhi41qMPShl4PF6UWv9c6A4ont6uyNY0pYZ0JEs2NBzpLMZ88MuLmj8bVXQHFQu15TfvdrL9hfECCwsV+rsDDPUviciqjozbNrJIGo5IdOalvOb5K5qbOjXDUcVsWcDJtqhYWb09rRe9T/tDElnausnsjMLxBRnd7YxqOv2qJW4m7JGbyhuTLlfy8OwuWIvTeN8A/N0ZGVV5jbV9BBvu+klU7dWz5sh8eX1juyRK/fezmm/skPGwpBCJmwEItxI0B5KSvpWrS+JRpC0RsOpIlCtIiAAIQK3Z4v/6xQIEbQ8xV3Mi2+oarQbxP78ooQStm9Q31qF9bKXOZtkSMC03+az39ouV10/Oa14dd3lkyFgFTB8eXN8OqB2g3t0LUa/LwFCMgN+z5mg/EPAwMBhj7GKG9EJ53bx3kLWiu235CFmK3TEZZWZq0t0yFfhNvTiZcTU80BSPaa1JdoYoFuq8985Vbr51gKnJApcvZ0gmQ4RCHixLEQp7GRiMYTdstNb89jfncDV8/88OMLwtwdxskZnpAvFEgJ2DHdQbNhfOzmN5DW68uZ9Gw2VkWwJXOzx+dC+puSLZQo2+gSihoId0pgxAzOehVm3Q2x+ly3HIpqvU6w5By0OuWGVhvswH74/xjWeuXyQ6TpfE8/Ko7zK5U68T0FU8u47y4bk6t90+TD4nfsylUp2B/hiTE2l27Ooin69hWQrLMkR0WXPw+S2CQQ8ffzjO3n09TE3kiUZ9RKI+rlx2moEuoF3RELhXS9gnXyVx05N84bueGw9EVrm9bKU+T8EdvesJ1yBThfM5uL1n4+dJ+hXXJyUI485esfObKMJ9a3i1riyNOF60toFHVjQfWl36oKX40z0SM/3fTstUbW0azvIylGJbRGhLbnPT3huSzWrUq/AUyzz/3Bk6Yh7uvHWAk8dneOzIHmo1m/RChUTCz7btHbz1xkVSc6Vm8INDLOpj9tIM28Peda8TkElBzNv09HY1Bop8dTUF76tWX3lw6vF46O/v/7d+GwDEg5bYgjgu715Ic+NInPnxHOVKo2mQvpRDIcrUBmNjORaSnQSDASYX6qRzDXZui+PzWUscUw2mqUjNFnnv7Ss8+Y39RKJ++gZixGJ+vEEvx0/MkM+IgCqeCOC4ermt1IryekyGhmJ8+N7Ymp9l5Yi6XQyUSVd4+83LhMLeRQ5rqVTnhgO9mJZBKVMBNAsLZQGmSj5wo+EQ9VuUbY2hHObW4PTsSRjUNFwqaHZsgU/YXqZp0tHRsaXfjfsUEY8mX1f4LRgISVrSB3PyXs5lRQSy0koKlsj2mWiMYiBOXz94TBYXrt9PiOXPwMZNLAB2RBVdfs37c3A8rUl4xUv02KymZouxet86YMcyFDd1wa6G2D1NljR2szs9U5ZI0YotyUBBz9qA3VCK/pDmd01e6Vrg9MjIZtbXWy9DKf5qn2KqpPnVJc2zu5b7ZILmth74xUW4sVM4oiuVwiBd2EeHlh53Ka85kxGF72wFDvV5aLgdfDQrN9O9ibU//5644qM53RR9wZdN84KN3SXay2PAvX1LxvUPDkjXZyzvciLDloBpq1oA9cM5sfG5pdvi2e8f5Cf/9DnlJsfTUAJMn/3uQY69e4VTJ1OL6W0+n7Us7329mi2LWGVnDN6f1TwyuATiL+dlY+czV4RAhC2SnUFuu32Il54/QyBo4fd7+KI8hd9vce99O5ieybN7dyc+f4j5VInrb+hrggNNqVQhGLRIdHTgaR4rn9diYChKrWoTjfrw+T2cPTPH+FieeNxPsjNEeqFMrWozM5OnWKyza0+SRsOhVLa5eH6BcMRPV1eI8+dTnD+3wNxMgUbD4Tvfv5FypY5ZMBm3TS7lNN8YKGM/918ImS5ONoV+/39j/9P/jempHH0DMSyPwdiVDGjo6YszM50nFhcl/sJChu6eCD3dEQrFKr/45+M88NAu3nn7MspQBIIWPp9FuSwjfQB/wEN/X5jaW58BUHnrP3PzM/9XGu66lh4b1m09sNE5HbCg2FhLIbG6RiLCKX3hstCLxP9WnCdClgDniiPrXvt5UHNkCtM+gWpVpqZ5bUKzNyGc2ZojqWqHetYHpmNFzbmMTEMWqnKthCwByl0BmcTMyp6EsHb45W/P4NUO0ymHE59P8sjjo0xO5CkWauzd30W1YvOrX5wgNVciFvPj85vcfe82Ll1YYHq6wHyqtEwk7CJTyOPzQrlJ+IRCUa3WmkdR0x/7OiXqKw9O24HNesKCf63ye0z29oU4PVVCA3P5GulUScCZWlIeKpZi2LLpMvfeoChHIpyImGTLDsdPzEg6VHN0biiFaRpYXpNbbhvC6zO5ejmDaRnMzRXJ5mtUHIksbIHKqu1ypWYupkytZRgfDFkbpli17xTXEgOVivUlSoCCSMTHpUqamquxTA+5XGWRv+VoeZLW/2u9nDbQnjql0xXqkQBTdphI2FqVPrR+tXc5l49uW5sDUIv+ozVHxsLpqoyFFNDpF/P6Y7OaI8NLXbm16mAnvHjF5XIO9ncsgZ+bOhXvzmgeGmx1HDc+LyNeg0dWKMn3d4jR/GbCExCxQ7uSX6N5c1Je1dUwsgnH7PFhg59ddCk2Vic9wUrwKHUlLzeo+Bbe31rVH1LcPyBdjseHl8Zzv7ykua1HsS+heOGK5pmdGwssQDYS40XNo4PgMQ0WqprxgnQ0wh5NqiKdvLgfwlYzarMJ+m5IKkIe+O2Y5ps7/nWmLx5DKCwgx3GsCHf2wO8m5b0lfFsDpq2KehUPDyoWKpo3J112xkP84G8OkZrKkW5SgPoHorzx2kVOHJ9dfJzRtJnbTJlcqLv8bkLSylZ767JuApHtQqPhMDwS589+eBML82VKxTqx5lTp008mGbuS5ZMPJ3nqm/vZuTtJNl0mm6sxPp7DUNJZNU0Dx1YUCmXeeuMy8bifO+8eYWIiz+lTc9z/wA6isQCpuSL1us3ovm5A+JtDwwlMU1Gt2FRLdUJhH7GEDwwIBLz0D0S54YZekl0hPnjvKrffOcJ/+V/f57t/epCjwyHMuSvMXf0Mw67j1srocppqapyuoR3YtkNvb4REPMDMbIErl9L0DUSIxXwE/CY+v0mt2mB6ukG9IZ3dt964xPmz8/T2hbn73u188tEE1Upj0Wv62e8cwFu8RLEwD0D16mdE01eg+/otnQura73vRnyLA5aiUN+6gjNkKW7sEu7xw4OKsi0UpemSdM9LDU2hviTMAtlIrsVXt13xT76jV9EfUhQamt9PiFvHSmCarYlIaqIkqXU3dSkCJsyUm3SDYbVI+zGUoi8keGD8Ug634VK0heKQmcvy3m+OccuDt+HzmVTKDYJBD3fds51CvkZnVxDTgNd+d5EbbhpYFPHV6w4vvHCax//kIB9lFbq5rgqHXQDx8dkSqhmG2tk+5vmK1lcenNbrdSYnJxkYGMDn+7ffrXRHfIS3W3x8VVSzHZ3BJVCqVi8VyWQQx4UMJnc+updXXjhDIVvl9KlZHj8ySqXSQLti3hwKeenqDuHzm9z7wI5FUOkxxPuwYRocPTrKiZLB9Jzmxk5IxoPrplQ5bC3FClaKgdboxDZBcSp3hYYDCadBIh5H62aWtVpKqzKUgNUWbWC91CUsgwcP7+UTFaDuyBh0I1sU27ZZWFggmUxiWUsoq+GI+blhgKUko9ljKLymxIQW6vKn15Txcd2BbRGhFwxuwKcKWYo7euHEgl62GPeFZAT29rQApBuTmu1RAUFyDix/zpWK/7mK5o1J4fQ9OizxqqWG+LHGvK20pLXfl0bz3rSIYyJeAadrWQ6tLAVLlI8tlGHAC1c0T25bLpJY6/1MlVgT1PQE4Y3Jpc9vKMU9/WJV9N3dipNpMd4Xm6W1q+6Is4LPgBfH4JEhTdKviFlL50JVW0yX5QZad0W046Ixgb0dimxN0remSpqYT8z9OwN8KW/Ta6kWML2nHwwMzuZc8nW4p//LJfskA6J2Hiu6vJIyeGh7J3uvV9g1l88/nV4GTLWW7k8+V8U01brKZElXE97fVjZK7eW6LtGYn9d/d4F779tBJdggEvURDEraU3dPhB07kgTDXgxD8ftXLnDroUEadRen4VKuS8CJdjW9/RESniC33DrI/HyJSqXB0HCcjo4gk5M5FLB9RwfBkIdTJ2epVh0OHOxjZirPP/2/P6OnJ8zAUIxEUkJHfv7Tj+jsCjIwGOODD8ZYSJV57Mgos7MFvD5DbLf+59vQb/xnDO3gFFNooFFYoJ6dI+uLUtd+vN4KkYifPaNdeDzC4z/xxSw9fWGSySD1uoPXZ1Gv2kxM5BgYjHHwpn46O0N4PAaGUoxsS9DREaQzGcBTn6P6u//XsuOYf+M/E33m/0bdWj6Kqdj6S1NQJopCBxqJLIkst1bS2bynby2j/2t7L5Yhm6pWmQoeHVpy+cjWXD6YFdFpZ0A8qUsNaBgSxxzxKnyG5ultas1I4vZpnwF4lYudm+PyQgVQ3P7YIebmSmQyZWKxAIODYT79eJz3376ML+AlHPYyma5hNVPWdN2hMF/gyM74Ku76F+N5HMdFodnXF/rKe5zC1+D0f8gKek1uGo4ynq7S0x/F5xN1oNZiJ9Uqf8BDz0CMn5yT0feRbUEe/dYBPIUi2UyFcNjL8EiMXK6KYSiSnaENQaUZD/NJ3qQ3tCRw2SylarOft5dvhcF/q7xek8NH92L7PHg8Jn7TYT5b5/qQj6DfolqzUUrGiOhmVKNh0t2kDdTXSV3Cdnnnt2f59vcOkFcGL17R3Nglo/BrKa+peGIdlTZIzvzJjIxnbu0WG5bPUmI/tTO28XMPheGtKfFJbY+vG22Osb5Y0BTr4i+arsln3xmFg11QacDxtChUbS0AyzIEUN7Zp3hlTEbfYS/EvQqfKXzUdBVu6hLFfnu1gKnbBHt39oqx/WaVr2ty9bVVrutVf1B8aV8e03xn98aP+6BpIRbxwuNtKvhjM5Ie1Q7MO3wCwkHxJzvgn85rhiLrA+yP5mREvi2qmKtoXrgstIChNipA2KNWpIzJc0kgBCT88Mx2xecLcizmq7A/oTYExX9oLQem8n6Sfoh64O0pzYODfKlEKstQ7IgqhsKaUsPFdsSrM5MuL/2SpimKkm51o+GQzVQZUctNz8eLmlNp6ZD5vqRXZjBkcfOtQ7zxxiXuunsb2WyFRt3FskwGBqN4PRaWZZDLVejrjzE1mWP7jk5ScyWKRVHRd3WHmBzP0tkVZnA4ysi2BNlMmXNn5ujtj7JntItKpYHjaC6eW8A0DEb3dPD+e1dIJsPN0XmD459P4zgSDPDYkT3UazaOo4lE/ARu9nDs3auMbEsQCnqZmSkwOZGjt16GVtdUg87P0B0s8NobnzB61yF++6oAma7uEK6raTRcDt0+hN/vIZ2WaNV4PEiyM8j+63rIZMtk01UqlQaW5WNoOEY07gO7QdgsEjAW0Pf95SLXv6GFBuI1XNplqxrdTLJTuGguZkWcs1UCzraodBc/m9esNy8IqRpV5V8mnHtvRnyO28Mz/lgVtERod7UgqVQLFQnqyNXhi7So8R8eXCmWXf0+lFJNCp1anPY5zfuOG0qg6GB81qb44hfc/+h+UpPzXD0/xfxciVLdwBcKcvjoKKdOTOO4MgEIeaRJUM5V8VgGtqOXbeQaztIdM1+1CflMJjI1LEMR9JnEAxYB71dLJPU1OP0ftMI+i319YeqO5rt/dpAf/4OIolqdQl/Aw7e+e5CCZfHMjiVgM9kwKJsxHropsTj6i3b4NwSVwyrB2Yzm41mXsFdz/RppOCtTqlbWZj9vveZ6nda8YfJ5SnPkyb089+szGMrh/KnpJj3hHP6AtUi7CPks7n5slPGGya7g1lKXhnYk+OYOF/uP6CHaqh0xxbaYKPl/O6Z5oB+KthzBtTxGMzXNeFEiNUF4UuY6K/zKZCJXS6zqP5xd+iB/uW/p5+03l7/at9YNQEZIb04KuNoWFXsTVwsdoQVM7+oTccCl/HJF6crSSKrTN3dc282m5go3trwJ71+heGaH4sSCS7YOv7gkWd3zFbGSalepg+a345qeoPi7dgUkpvaFK5rv7F4tMCvUNfNV3XQhELHbN3bAP57T/GD35p/BMhS3dGt+ekG6rfc03Qxsd/3v849RawHTsaImUxVnhusiitcmvjxABTlWcZ90jxbmy8Tjyyk57fsQx9YkOpbEjxrNF/Py/TwypK5p07KyLhc0WV+IBx/excJ8iUjER7VqS1c16ica9eLxiK/ozHSejmQQx3Hp6Q2xM9JBeqHC8S+mSXaGMAyDfL5Gd3eYrp4IHq/FfKpItWqTTAbxek26e8MYhsF8qsjZ0/OYZpojT4zyzttXKJcF3s3NFnnl5XN8709v5Pjxac6fnSefrwFw4GAfFy7Mo4CF2TzD8X5qp14FWrQgcN74f3L7Q/8PPvjgM+594EbyuTqlcoPBoRjJzhBTkzkMQ9HXH6OnJ4zrujTqskEPBjwEBjySGGWZOI5DMhlEeQwc7XKh0M2FnEx3TAUJr2JvB7grzgPVXHcu5TUn0+IostZE5lIOdsZksrby+vGZwll/eWz5omoAjXKdc1dmyFQs4h0henojXK6ZpCqaZzZYKxquUGr8FvSHtn4RjRU0Z7PCkR+JKIbDsC0KL16BvQlZJzabZLRTwzLpComOAF3dYQyPCVUbVytsM4BlWeLOUTOJxALcdMfO5v2sSiDooeFoPvpihkq+Stwn0zRXi9jSF/Dw9vszRCNedg0tTRhvGo7x6ViOah0mMzXm8nWWbmkaj6m4a2f835x6+K9ZX4PT/8HLayoG+4P89f9yiKmJHAvzZZKdQbr6otRMt6mIXTph7+kz+DilGSu4ix3CjUDjRNHl05SMjb+zR3E2LbyddrHIRlWzXRqOS9i3tVNpZae10NB8tqC5WnAZDsM4Af76Lw4ugtdo3McP/uYQmYUixewSmD1ZMpjNuuyJm1tOXdJKsYmt55cuA7FVenqbWCvBcgZre/lNsVFJV2XcNBiG0xnZ5W/6Okpxew/c3EXzBnTtH8hQivsGNe/PiBAo7JFOga01ploCpq9PCa9so1Io/nxU/nYtFbIEzL06Luffdcn1gdTFnCR0JXwKS2mu5DRvzbAsfav1bhI++f2zWQGncZ/i7j4Z7XWsGD823FZwwNLz+EwlvoRbyj0UYPrQwHLx2r+UjRmsD0wvZuGhQTg2A8ku6dr+oQAVWLR0sixzUWS58sgEQxbdvWG01tiu5q0p6Sb3hjSfzClSVRdLCR+26oBHgYNMDfbE1z/BGq7m8wWxhjMNi6FootmthaYHG7WyzdhYFrthc9vtgwQCFpl0lbErGQJBieq85bZBZqYK5PMVenqjTE7mWUiVGBpOsHN3koVUiZmZAqZhYFpQr7l89OEErqspl6r4/Bb3P7iTfL5KLlslEvERCnup1x0+/mBy8fTx+y2CIQ+FvAhbkt0Rwrufwr/jtlWfTfnrPP7kDcymGzhh6QL7AybabXDdDb34/CYaF9dV1CuaXL5KPl+jsyNILO6nVKwxtZAnFPZx1g0wX3FwNIyEFXf1bu4Y0XDFLSFf0zw2tHZnWwHpKuTrLgtV2WisLHHo0ItuHSaQmi3z3C9OUC6UsHx+PF4Lx7J47OgoT29fQ52IUGU+mxc7saGIYjC89S7C6YxmqqS5o2f5eP4n58VveC1K0MpajxrW0RXi0cf28NJvzoGztJNu6Sosr4EBDGyPU00qQq7Nqz//HKfh4rhQsoXTbbgu1ZqN7cLHx65SsaEjZPLNp/eR7A5gmQaJoIfpuoNGqGEoA9NQ2I6L7bhfKWAKX4PTfxfl4hIOQ3IoTGwgTKZY4/hsFo1BIeGQCHmI+C08zXaNqUSx2xpPtI9VbFfEO3NlTb4h9j2Pti1O+zoUpzMumaom4V//YtBaM5GpciUlpt37B6Ikw1sncWstO/aLOenU9gQUH89pvrkTFEvg9UJO89acy7d3JTDaaAOpeZdMDV6fdNm+SZLGl0ld+jSlyTQNwbdFFD4LBkNLMMZjyt+d5oiz4UrnseqIdVNWGink1rCADViKH4zKSKwlDHh9UtMf3Jh/2V4b2Uyt5DOtVUZzbN/6RGezGtsVDmwLmO5LKE6mt3LgvvyieXef4vgCvLuB/dfOmGJnTF5jICxj/v7g2gEEd/YqDCURna1azzQ8YLHqvHC1WINtpfN5PiujvndmxMbrX/rWsREwvX9ANh2Gkg+U9P9xAGqLK/7qy+c5fHSUl144S7VmL3YBAwEPT37jOnx+k3xDYlxdZBM6FFbsjMONHrXqfJ2vuDx3RYQqh3rWfm/HFzTXdajFqZDruqvM/62AyZ7RJJWKw/hYnpmpPD29EUa2J5FYSU295gg/01DYDZtQ0It/0MTjMZicyMuI3DQJR3wEgxapuRJ9fVF27+lieDjOb148QyFfwzANOrtCpFJFbrxpgI8/Gl/0F/X7LR4/MsoHx8S9xB/wMDCcoBrqgnXMYCxgsEfW6M/mJcUoaIHPdZnJyfo2EgG/R9HdE6KjI0ixUOPypTRKKbp7wgTDFhMLmkh0vUjPFd8nmqt5+CSlublrM0N9xa09mtcmVm/s2ms0Lp36I8OKfK7Oj/7+UyrFsvhalW2wvERjAd5++ewK4ZxmsgQXc6L4v6NX0eFrXXhbP1/3JWDfCjeNkq0pO6xyPJkoaaaKrDrnamtQw2qO5tx4EZcx/vT7NzA3W2A+VaSzK0xvX3SZrmK6pHFcTX/c5Ikn9/GzX52m0nCIeiV8plyzefTxPRz7dJqqI99zrryUpBgP1OlL+CnXHWxXE/SajCSDzOSqTDSbL1qLndZ8oU6uYhMNWHRHvP9hQetXHpx6vV6Gh4cxzf+x+RyGUnSF5evqi1r45hTjmSpTmQpTmQqWaXDbjgQaiWm7NeYyfim3OJ7o6Y2gfSa/HdNEPfI7Dw6snV/c4YOrxY3B6bmZInP5GsJYMnCuAf1VHc2bk9KxO7pNxknHFzT94aXRktiKyO/9yQ61ijZwqFvx0pgmXdXsiIU3FVptJXXJsiy6urowDIObupa6zuMF6ficaCryLUMW04ApnnqtpoNGlKcRj1iwdHjhahFevOpyqEfR2XY8LUNxa7fi+IJL1Ctegj+/pLm7T/z1rhQ0EY9ajH7dqBqu5sWrmqe2K9Dwd2c0d/S2OrGbP/5MRkD4Hb1C3H9tUnN9h2K8uHkS0B9aAUvRFZBNylbea1dA0RvcGHzPVdjSjXq6vNx/1dWavzuj2RFVeD2exXNhrarYIqS6vx/emYHPUnBj13Jh2h+ztgJM5yt62SH8YwFUX8DkjrtGeOvtKzz2xF6qRfEnjicC7NyVJBr3slATYHpzt9qSJVZnwOCZHZpfXtb4DM3BruXH2XblnLy5a/P3Vynb/PoXpzhwUx/BkAfDVFy5vEA44qNRd4jG/FQqNufPpujpjTA8Euf0qTlmpopUq3X8fg+xmJ9Ddw5hmgaxWIDtOyCfr1GpNnj6meuYmZZJjuUxCUe89PZGcByHkZEONODzWbz3zlVSc0VCYS/Pfu8gwZDF2mSjpdJao5WsN67WTJdcEV8q4WVOl8S/19YaLIgmAyS6AjiugHUHcYzYSpVtESP1BBSPDat1RZHtpVgSGa5Xu+Pi91ypulw8v0C1UhdgCmjtolyJ1bYsoWBFBuNcyWku5zUDIcUNSUj4/hAezHJqT9mWdL6HBpcoUS6aNydgvib3v99NaHqDiogHRqLGKmqY09ykJv2QTZVIzRUZ2dXB8M44IGr79qWxP9R6HaGuPfv9gxRSMuoPBj1oDe99Ok0xV0Fr2RiXbUiXbOamc3jzr5K4/gkODC0XKbQ2Zhr4dCxP3dZUbRdQTGZrXF2osKs7REfoP16i1FcenCqlsKx/f4dhpDOARjOZqaEB2xFRzXzNYK8q89xPluyfNBrXNHnw8F7u6A0S8Swpzteq+wcVPz6v2R7Ra3byJjKVZcA07Le2fHFomrGGnYq+NrsPjRDl2+uDWQEZaykpYz7F95pCGgNjy5ZWG5fCMMxl/28q4S6t9bvrPUd79YcFWLw3oxkIwc0rcqF3xhSfpMRjL+mD565IFvrNXWIE/+q424zeW/9G0nJb+MVFSSeyDBELdfsVyU2SmU6mNcXGEjB9fVI62WFLbLEyNTif0+yO/cuArrKtOb7QEuAt1UoHgvaqOIorec3BzrWTrQ4mFcdmNIW6iHIarsQWth/DQkNzPqu5q1e1Pa/8ef8ArD4XltfZjKYvBMMRg/6QptDYupjkWmurwPTjlObBweXv4Y8BUF2gHg5w24N7cLIFlGsxMNRNoiOIx2cIML0qCuzNbMfaK+5T/MkOxRvToFNLmellWwSA0i3f+PlMpbg6Kd3Pmak8F86nufmWft579yo//E+3ceVymo6kdDxjMT9zs0UmJ/McONjH0FCVVKpIOOylu8lBnZsrcvLkLPfdt4OBoRiv/OYc+6/r4b13rjbH/HUcV/PnP7yZ1169yM23DnDgxj4mxvPs3dfN3fduo7snTDzh3xSYBmiQLSkmx3MsNGOpB4Zi7E1YBC3NcNTEbCq93eYuca3u8Xplu0KHaYn0ig042Lk1Y3op2TB6DUWpodfknbbqlm6DmZQI53Rz/L0IZ10blEG15nB1tkI0Fifhgxu2L5n7V2zNlYKY+vstTYdP3Elivs3P2XRVLKUKDZmEeAy4rUctAkaAM2lw0Hx7pwFoJooiVApay1X5rcrU5N9b13Q2U2GEBK7e/Ni5QCxsEo8k2G4qTnw6w7F3x5ivSjxzq5J+mXDOz+VInH+ZeP9+6L6eYkOsCyNeRbntfpavOmgMTNMiGfKQLjUo1x1OTxe5Y0d8maD2P0L9+0Nlf+RqNBqk02k6OjrweP797D4sQ7GrO0TEb3F6ugRK4TUV3YbDP/9mCZg2XFFSByyH9145w7PfO4hnky6xQnFXj+yyHxkS3murynWHq6lyU8cvZvwHh2Nbtr5QKO7pW25l5GrNiQWJnWuvK3nNN7YgtNFK0dUb5NvfP8Ds9MaWVhuV4zgUCgUikcgftZO+LarYFhUR077E8nH0ff2K1yc0v7rscmOn4s9HjbafacYKig9nNR/PSab8ZynN+Rz0BeHBwaXf/eZ2xX8/q/lgTkDwQhUmyxD0yIh7oiRczLv6hJd5NQ9VV1NuCIfVbgLT65LCnX1rSjiDjw0r3pkG+JcBqJfyYrc1V4GYTxOyFI7WvDy2GrC26p4+Gam/Pwu7YnoxwrZVwxGYryq6ArC/Q2I5fzuuxbM1Ir/7i4vSWWnf+IQsRdij+c2Y5tEBd8Nz4XRTRPbziy5PblckvqRf62Z1rcB0rRv5HwpQxwqaS3m4r9/A7EgsowqlW8C099qAaUt8kp8pMLhQQYUCFAIRvEGTN6Y0d/aodT1wa80ENcuAWh3mZoqUy3UiUT/VSgOPxyQY9HL5Uoq+/hilYg2PxyKXq5JJl7nuYC/lUp1Sqcb+67oJhnwszJc4f2aecMTLPfdsx7IUH384wfYdSX7/ygWmpgoABAIW3//zm3nztYvkCzU+ODZOoiNIZqFMLlvhyuUFKhUZ4W7blcBZZ2Lj1zXGJmv89McnKFcaizaBfr+HZ//0IB3JABdOp1hIicZgYCi2qhPbyrs3lIjwKg7MlCBXl+5htgY7YsLx3R1fipLdSl0taBaqS53rG7sUr4xrjo5Aa8PQThsbDMOHZ8vE4z7Q7iIwVQDaReGiXE1vV5CpmiZX1WTr0i2eK4t/9WBI/E1trfhiXr7jXB1CHtFErOuyosQuLr5B9/V0RrMrKtZmSb9isC2TYKUHd+s7a5+qxBMBGg2bVC5PZyy6pfuD1hrbEc42LDmpLL1tRchyiHmKpDMZiq/8Z8qH/+8MxSxen/Xw8KA0nBQajUHQZzGQ8BMNepmrwA0dLp9eydJwXLLlBomQ5z+UBdVXHpy6rkupVCIej/9bv5UvVUum9NJrah9PuMhuMuEHE0Wj7q7rR7jqeQ2Fx9Acm4X7mpypbLnB2ekCWjugNS4Ss3qtF8TKcdKn83CgUzxIXS272jcmRfEcWWf01HA1VwqKbd7l6sq+/ijbdyawN7C0Wq+0dqnVqoTDIdpjURff5ybCnY1LhEbvzsgouF0088Cgou6KmOXTlMvehFgXmYZiOCJg63xWvFZny3BLF3yckq5na3xtGWqZOn+uovnNVc2FrCSg9IcVDw0q3prS1FzpxijgQFK4pu3AtJXvfiYtfLK7+/QfCaCuPboPmPJZ9iYUobBoXdZK1mo/lj/cK2P54wua7iBMFzW39rRAouKmLs35LIwXhU/rNSRFyWMIPcTRa3vQthJi1jsXWhnepgFPjSjem9H8/KLmsWFI+o3F7oXj6i1RSTaqPwYwbVU7QL2/X+PfTOnWrCVgKq/XTq/J/gHAdKX4xEXzHjLpuL8/uK5n7kJVIkFtR7iYt3jLRKI+isU6fr+F32/x4Qfj3PvADt56/RK33Q67dnfjOC7hiJdER5DJ8Ry27TI4FCcU9mEYAjpHticA+Y5z2So3HOjDdTW33znCwoKAxKGhOCdPTHP2zDxoCAa9JDuDHHtvjNRcEbvhEk8EGLuaoSMZJBL3rlqDLCBXNPnxj45Tq9ooxIvV1VCuNPjJP37G9//8Zn7x0xOLj/EHPHz7eweZ9Ye4knfFtUXJf2Vb1O2WkkS0Tr9qBlzAl+WD94fgQk4zVpCo5B1Rxam0ZrYCfYHVqva+/ijdMYtKoYa3KZxbNmx3bIIhi+GBMDv9st7NleXa35tY7T9tKoTiZChyNU19g4W8w9fqsMsUaOX9pdTQuC4UbLHTW1krPbhbx1YjdAplmbjRMD+9YNMoVCEYJOST9ozZ/A5qjhz/+wYUyTb6Vvtzmw17kQLWcMFrgNd06famcaNRjAN/gr2Q49SpDEM9vdQrMXZ2hTivIRawiIX95OqK308K9/T+nqX15fhkkb6Yj9HeLcQK/juprzw4/fdehaos7go4P1ukurA0nlDIxdN+PbanKm1UQ2FFp9fh1LzNuRkbx9UsFOugxdi6YMPpLNwz6v+D3v/ZrOZ0Wi+O6EsN4Qw+MqToWVvYCTRVorkyP/3dGRptK1d7TJxG7IIKDQHx5eafW/U5XahqPpoTxbqlFAmfWDCtpVrdSm2LwlhBcTkPu+OrP899/cJZ/WxeYlAfHaQZParYHW89RsgUIxH4+SWJ4Xtm52qg0R1Q/GCv/L193LwvAV8saA50wqcpufGsBUwHQ4ozTTGUYgmgKjS7rgGgaqQL+sig4tgsJHxLgLrY0IzlNQ8MKi7mhfuaqgjnuWILMBqOrP1ahhKFfMgSsFK1ZUOzO65ZqMiIvi+k6PDBd3dJEs2bU5ptYZgsweAa0bYVW3hwrcSplTVWEJ7pjpji2ztlJPnIEBTrCq/rkpuvMDtToJCv0dUdprf/2jr37bUVYAqy2Tk8sjEwbVXSr7i1C351WXNPv95UxbwSmLbXlwWmsLb4xEDhw+GdV87wne8dBGs5inC05tgMLNTkXApaiqqrOPdpjv7+KF6PyWefTHHPfdt5950rvP36JR54eCfahS8+m2bHrg66u8NEoz56ekLUazYNWzMznce1NfEOP13dISwTisUGtaqN1prxsSyO67JnXyc+j8Urvz3L6ZMpTFMRCFgcfWrvYtKd21xUHdsl0RHk1ZfP8cTT+7DakrMs4P/P3n/HTHKl6b3g74TLjPTm8768IauKZLHova0qkm2n7UyPpiVdjTR3Ad0F9g8ttICAC93RP3chAQJWwPbeGWlsT087et/0LHpfLMeq+rxPbyMzIs7+cTI//1UVTY/a8AUIkplfRkZGnDjnOe/7vM+TXXSYmy1Sb7kHeSsmY0MTlEsNFufLdPVEmJspIwTUa01++g/v8y//9Br2p3U+TxPixUOV76/uhPczanMMcEOP4PU5uM5ubSyaKyhUps5Xv7aHZ198j3uP7uKpljthO4K2zv0P7CYQVM+Dpa3OXq6NW/uXf9+lNonOVuFEdj215dlJyU19m9s4w2oN7mJFNfyVGhAKKg3ueMzgm3FJJiNIpzUcBBqq4vTclJrTkgF4ZkJy38hqgNw+9t///ASm8AhJtc6ZIbjr5jTaib+icvB/44nHTuH6iwgzgAxmeM4M862v72Zvf4xXZiSZvJL+u75b8PSkJNBq8vN9Nf9nyg3gS3D6ZfyGRG88QLHmUqi5lKoNwmGThudj6RoCQXwNj3Nl53rT82m4Prom8HxJ2fEoVJvUmh71pr80+dSQtBlEPTGLgKkxlqnjS5gsbw4g1oaUyr+96QtqrmSqDAj43s5lLcSotZl4ueTxccVFOtgJ0vF56rGThMXqpb/R9HjiMdUBOeFovDwj2RKHuKXs6pKXIChQbkjGC8prfGdCKGcXX4G5pg+vzvjsTCiR5g67Df4u3tAjEGyNS05m5RLQXBsxS4HUrRXlEDUQgWu72+BA/f2pnJIa+eEejb884fPitFwFYhq+5FROgeGVmeeZiuRMQXJ5Cl6dhTsHlkv5vSHByzPLwHSuJlmZ+GsD1BenoMuWxC5Rk0sg2BlXC8SepOCjFdneiqs4XyFDUHUVWAoakHVam6OL8GUbnuSpcdibUuX79xYl7y8qj/avblntkhTQ4Y5++OlZJRp+5+D6432UVY0KfWGBu0HmtthUHOGVvGEDAeU6E7NlHn/kJI7jKUczTdDRGeb+r+69qO/82rhUYJp3fEyNJT3jS4m0rYTInxyX3DW0uTnBrwuYCrG5LrGGwN2guuNLJW83EhXc2Lv8Yy1NEAgYlEsO9x7dxYvPn+OlF89z+Ogumk2PhfkKqXSIK67qxbQMcvka2YUqUkBPTwTT1Einw4DEskyyi1Xm5stYpo5p6czPlYnGgoRCJm+8Ns7IlhT9/QliMZt4PMjwliROvcnrr40jBEv+9nbIIBoLMjdXZmKiwI5dadXQBFTKLm+8NkY0pjb1K4GpEMuqKplMlc6OMNOTRYxWlrtebTI1UWD73s4lDuqvI16cht1JSYetNpDteScVFOyJuDzx0Ekcx6XZ9HBdiaYLPNfnV4++zQ23X86xZ9/l8JHdlCsehYJDIhlk29Y4oXM/wej87jqnqi8qDKEqJZMVxdsHGC8pW+XzRYgFVAbV1FmXqV2pwX18tIRbrtHTsZoatnJOaGumPj8lubZHLD1Hdw/BE+OSfWlVdVp57K9/Zz/1bJlKQdHOelKC2vP/J86O+3j04VO4ThPhVdGD/VDJYMQs/vGXJzn8zf1sienc0rc89i9Pw1OTsLczwdxiAd/zMD+j0cVvanwJTn/Lo+0mNV9y+Hi6wkBHBKkJak0PQ1+zQFvGUuf62fkKU7naOngkVgBRXUA4oJOwLSxDELMN4rYiYkOdKzoEP3k/x/beOF/dpiMQG8pXVVzJc5MKFKSDy97kN/RC+BI6RttndmRIAYifn4MDvlrgLAvM1uIppaTZ8KlWmoyN5gl2JXhgq35JgHRlBHXltOO3OjIlyvquPwKf5OF0QWXmQoYqHe9JwetzEluX3Np/YaQwGBGczkvenodtcdWYFrUUV6wp1fdOlJW9nm0oya8PM4IDHcvH2BmHX5xXUmB9IQXS/scJxb/ckVDlt7ESHM/Cd3YogDhTkXycUwDxvUXJzb3wYYYlYApQdyUDYY3Ros+L0zC0JrMhENzcL5cA06r3Vtx3oPXfkpM5n1JD8a1em1Oly1+c9Tk6okroH6Oam5o+SwoJG0Wb7tHeCNVc5XBz24Di0p3IqYyprStQt3LcSyQND47NKrDfHVLX4Nru1d8Rs1TGtupK1g6ZUkNlqVdmzXWgmG8wPVVqAVO3dS0ANObnKzz08Alu/9p+PixpLZexC4/3SwWmxYbk2Jzi0L4yAzf2XrpSQIctuG8EPliUzAZYqlBIqcZ01hGMlb54YAobN5+sjZXVHV8qK97dGzQF6kLS2xvlsYdPcNsd27jrXuXadPLEPDt2dnD1oX58KSkVmyyO5QFBIhEgGg2SyVSYnCjS3ROhvy9GrdbAcZr098fRNJ/MYp1IxGJmqkhnV5gbbhphfq5MMGTS1R0mEg1iGBovPX+WXLZGpaw042zb4P6v7OXYK2NIXzIxlmdoOEFDF/hCZ3aiQLFYZ2Rrio1YH+3XUukQZz9ZbL22zODMZKrs0n69KhrX9cCr0xAwljeoQihJr2auTKXSpFisK9qUVOdsahLZ0GhWstx5fYy5whyBpmD3UIi4OYv7+P9OcfEcwW3XQtflv5bz7mjZ7z49Lrl7SM0Fz07CfSMqqfDkmKTuqerRRnOND8y4gnOBOFcOJBmIyqXXNwvXl3j+8rFSASXRN11Ze2zJhyWNu7clsLQkTrXB5McnKSe/RoA+pO2ge/P4bh2/kkULRjD9BoamoxfL66h4l6cEexLw6BjEPWWlHDR/sxWHPm383oNTXddJJpO/8VJSF4uuaIDYVoPzi3WO3NfSI1yRnQgEdA4f3knedfEKTaZyNTR8BGox1TVBwNBIhS0iAR3LUF34GwmKp8ImWzttzi3UuK5TMFWv89J0mANRj+x8ifnFGr2dNsF0hA9KOp6vuvMv1HF+aSG4PAWXpwXvvFnD0FQG0TTAdX3y+Rq+LxHA1HiB2plFrr12CO0SM1eaphGJRLBMnZFWKWkt2O4PS+aqgnBAkKlJTOHzyKjK6kgE50uwpVUG2wioCyG4c1DndF7yyKivuE0SdiYFH2UkMUtNnt/dITA1+NlZ1Ym6PS4Jt1Jkuib4g22qTD1XU131tqHkwU7nVfPArf3wVyclL8+AhqTuLeuW3t4veGVGrgKmpabiEkokL0zDlR2KXrHuGq0BQGtdVRIJm3giyPx8mWDQIJ6Mcq6mkamr3x8xlHvWT87AV7YowPXIqETJc26cfW4DlKYP42VoeoqDdk23IGTA2wuSTE2BeVfCdEU1Q7Xj2Iyij2yLKx1XgEfOwy8qPg+MLG/gdiUUtSFkqFJZJKLcgoQQTFSUJWxbUUIDKuUmo+ez1KruqhKmlOB5Prqu0ag20EoV+iNxPsqqrPVmcanAtOFL3luU3NqnyttnC/JTA9REQHBLv2C8pDq5XU9ytqh0IbOOUn34ooEprG8+2fDcWtWd9n3fnth47pBSEolaHLp2iJdeOMeNN2/BTgTp6hogl6vx7jsz9PVF6eqO0NXdie8J8vkapaKD6/p0dYUJBAxKJYdm0yMSCyKQjJ7PI4TG6GiGnbu6OPbKGB1dYfbu7SYSCVAq1cnna6RSNrfdsZ3z53NkFqskk0FGtqao15tMTRbwpSSeCDI5USQTT5AMCRYXKpRKDj29UYJBZYu6MoRQALezM8zMdAla84O6FZJ0OsQm6mafOdq2xW2nNEsT3DYAp/JK7sipuizMlWk4HpWiQ7FQX2oakrLV6OM2KRQl83mD2Nn/H+lqHj2UwDuVp15aXPqu4gs/Ivb1P/+M2dOLV6eGourZfGlKEtDhztbmteaqxuCIcWGZuWcnYW9SMlvxeW1W0mFr2Kbg5u7l9WGlvNyhLsHz05Ijw8u9CGFjvW31Yk0BZB2Yn63y5EPvUSvXSXTGSVTLZEuQiA+iiUm8WgE92oFbK6JFAptS8XRNUd+mS5LOgMRxf407lv8J8XsPTg3DIJlM/s8+jS8kgqbOrp4Qs4UG3/jW5WQWKuRydRKJIPGOMDNVh8p0FYlAa2VHB1JBtnbYn1rIdyhlk6+6ZCtNrkhJUlqNn/z4JJW6pzTgJIQCOt/+xm46u0KfiXe3WQigK20TNlWmy/N8spmq6l5tPcCRiMXsTHFJ5Fi3NbI15UyzWSlT03TCYZUuXAm6FjM1rKjNzi1x+nUPmS+wuFBhqDNM70Ccy2Ma05MFstkaxYRNfiBKLCCYniqu0pnV9dWvPdAf5e28xi2tTN+hrvXndWu/ahZ6bBQOdSv+a1eozVEVS81qG8V3d8DZIqQDChR/mFFyPxsB05dnJLf0iaXruyclWJhuZ9E3uV6sbmxxXaVnaFo6h4/u4p23pqiUG4oDPLy8QWj4ktdmJc9MSu4dgiPDgl+ek0uNCGvjtVnVbTwQURngiCkImaoh6qOMpMsWVAw1FoaigncXJAs1yaEuQTyguG2jJUUhaC8g39gmeH9R8uosq65hu0Nc03Si4QhOzWNyNsfCTI1Yr02zVeZrOj5TEwVqtSaFQn3dOcvWhsR1JflsjcuGEzw97nN5SjUxRS2WjC9en1OOViNRcVFgCjBZVlnnUKu0qCSXPj1ABVZQcpQrzxvzKjtsrQFAXwQwhfXNJ2ujrUvs+f4FgWk7DEujuyfC1dcMkc3WEBoMDibYviMNAlxXUqs55HI1TFMnGg1gmhqRqIltWzhOk0bDRzeU04+UkpEtKWami6SSCkAduX8P83MlpiaLdPVEGNmSQgiYmiyyuFihWm3SPxDHsjSKhTrPPXsWTRMELAPbNpmbLXLz7hS6X8frDFF9q0E+V+M737+Cv/+bd6nXW9JLEuyQyTf+YB8vvXCudcFAtO6FHTLp7YsxO1khGrUI2joXcf+9pDiVg42UAPckBMl6lf/rr05iSY++ngjbd3Tguh6arq1+XjUDCUSScRJ3/q/49dLGXybA0nw28CZZF66vqCULNZirSm7uW73pXBuZulSb87iam+Zry9zyTwpwsFOZqbiSdZURUG55ANf2aOjAVVGPqYkcCwsVzmTDDA7FiYUjqxQT4q1M6WOjktv71f8n1iQ2xoqSF2ckV3UInJrHk49+TKXqUSg0acoyQyOduK5HNlsj3TGIHu7A8TQ8LPSGRyhqb2o3WG54S89qOvLbozZ0KfF7D049z6NerxMMBn/rs6egFrE+1aZJdzqB68H5xQpnsyVMXZAKGzRcH0PT6IpZ9MYDn9lhotESA04HTJ5+8ASW72NZij/Y8MEWHk89utYV5PNHe4EzTB3L83AcbxUwDQYN7JDJxGSReNLmzHiJTDROQOeCTVa+79NsNghYATIL9VWk/6GRJClb52c/+YBaq1lCCIEdNPjmt/czfi7D6PkcAO8ZghtuGuHtD2aoF2qAKondcNMIH3w4w8RcDU2ohfgPvrYbS9scvPeElC5hxVVApe4pLuqRoYs3CtiGyjRPV5RU1029ild6+SbANGwo0RJQ2ql9YSXXdG33xgB1ZWOLlJJioY4vJY7j8sTjpzhydDcvv3h+aYPQHgNtYD1T8Xl6HL6+TaALSbHR7jBeHfs6lrmzvWF1jj85o7jOW2IKtH5SUP+/N6X4ZV22INY61lBUoM2qkt6q46bh706r713HoZWShfkqTz6mZNkydRgNCixL576v7FWyMvMVwmGLeHzjFVNK0HVBMmVjaQo8//eTPgEd4hZ0BFWGsunDdd2C7YmLA1OAqbJcJ7z+eQBqOzpsxfd+a05ydZfAaj2yXxQwbcfK5pONdIlNW+e5Sf+iwBRUyTWWDBAIGmQyVWqVBp7r4TRcTNPAtnVsO0QzqtQT6rUmhqEzP1/mxPEFBobidHSEcRpNdE1QraoKTCweoKMzQqXsMDNdJBQ22XNZF8Ggjmkq2T6EIBA0CIUsPN+nXvd5/di4snj14cj9u3ni0VPcePMITs0jUDzHQP8WGg2fYtEhmRT84IcHWVyokFmskkqH6OmNtrK3uSUlFiEEQdvg29+7gobT5OUXR9mxs4N0Okxvf/hzAVRfqibAjYwTnJrHs4+fwpA+ji8oNCSRaADLMnAaLoauqcwugKYRCBrEkyG8voMXNGUpr/hv15ct1YE1Y7yiqGA7EoLesMpQWptwKiWSh8+rTn3HU3P8zoTghhUbvYmyeta6L6DxOhxR1ZNjs7DFrfDTH7+P47Sa1nwl8/W9PzxAd1941XzdZQuu6oSxEuwPrE5sjM6qRMRXBqLEIzpj5wrUa00K+RpSMymWPOywRSBg4TgujuNj6AFKhQIYFgHPw3c9FueqG3LXda+5pGzRG7+AjddvYfzeg1PXdZmbm6O/v/93ApyuDIEif2/vCjGctglconzMpYYmVLZ2cb6yYpFZlv3wJTQa3iXLV32aCNg69z+whwd/flw5ZqwApvceVTaCAgmeh1+pc9ue5EVdrHzfI5/Pk4h28OSjJ6nXXZoNj6brc8UVffz1X75NrdZE1wW+J9F1KJcb/OTH7/ODPznIxJkFjNb1eOLRExy5bzfPP38ekCwuVnn0kZPcfXQ3U/PnFNiSPk9eIngPG4L9Ld7pYER16t85wEWb0RZrCpje3KeA6b70sgj3WmDaDtn63J6k4q9uBFBXNbYIaDa8Jd1FAKfuUi45hCMWlXJjwzHQG9bIN3zOFJSF5ScFNmyGawPTDzISQyhAMhQVXL9CQH9nQvD6nGRfWuPOgfXWpl220oZdGZoQ7EtLPinIdeYITs3j0YeO4/vKtSygS4pNSQyP114dZWRLCitgoBsaYXPZd37V8TVBKGzS3fKd/+Y25brVaauGjHMFySNjkpqrLHIvBZh+lPGZr7GhBugXAVC7bNWF/NyUxJNKgsuTsCPOFwJMYXXzydzsal1i5WLnsyd16TQgH2iYGvHeOHqmSixmUik1GR+bI9URorMrAj7MzRbRDZ1wJEAgoDMwGKeQr1GrNonFbXy/yexMGc+TnDie56tf3a2oKQmbYNDAdV2kVLrP41MlFuZK9PRFSaVCVCtNFhYq7Nvfg2XpbNma5rlnz1AqOXR0hBkfz9Nz8m+xrv5nfO+PruTpJ09z483DFAsOHx+fI90R4o3Xx5meLLJvfw//8l9fy/RkgVyuRrozTF9/DMsULC7WMXSNcqnBsVfG+P4PrqRq6hQbSprJlax6Li4WH+cks1W4Z2j160II5mdLShLK9dBciazUVfPZfbt48tFTuK4HKJe8QNDg8JFdeL5/yVbREsnDo5I7+gUrcVXeUXzyb21fttXeLBqe5KkJSW9I4Er1jA9GWWqwbcdAWJC6iLCMrglu7BPUy01+9N/ep1prLiUsDQ0aTpO//7v3+Jd/ei2h6GroNBhV/yxXk06QqXjoQtGEPrB0vvL1y8hnqzQbLr4w8D2lB/v6sVHuPbqTJx87jev5aJqGpqtN0OGju/no3TFqDvzBdw+sWyd6gx7zjhqT5qV4Lv8Wxe89OP19CE0IAhv4kH/e2NIZ4vxindxshXrTwzI1NJSQed1THMhUQF6yfNWnCR/o7A5x/1f3kl2sMDFeIJEIEgyZHDs2zuJ8RenUuZJkyr7kCdPQdRbnypQrDbKZGlJKBofiLMyXqbWkX9ocsDbOqlWbLMyXCYVNigVHWa9Wm+RzdcJBnVK5Aa2MYq3s0J20qJbVsS4E3k9kJXtSsDZr2RsWfGOrAqhf28oFBeBfnFbOO+1/tykNmwFTgZLMempCcu+g6oI/kVXantesAKhrG1tcd/0FLhTq2LZJpdzYdAx8Z4fghWnJlqgqvR3oWO9clq1LXp+XdAQFjgTbkKsWYNdXEk/3j6jX1gJTUFmNmClhjQxWpy14clyusslsL8yNhrfkHhc1BYt1SVWTFIsOgYCBEILXj41z0y0jSzzvNkDVNOWHfvjobgK20eLoCQZXNJltjQuSQWWI0NUCzhcCpqfySuHiygtYen5RGVQNSTwoiJlqDHzR4QOmrTO4NcmwSOJLKDo+L034XNctaPjKwavUFORaOpcaLHG4w6YgHVQcw8mWlq2te+xs1klETCoVh4GhJKVineMfzNHZFaZ/KIHb9BkbzZNZrNDTG2NwOIlTcykW63xyZpG+/jjlUh1d13nppQmGR+KMbE2g6zq+b3DmdJZqOEAqFSYYMHj26bPsv6IX6fuUyg6xWJDunii/euYMExMFvvXd/bz0wjkGBhP06gbVx/8j8SP/ka9/8zKy2RrxHpuunii5bJVEIsQNN44QjVrkslWGtyTZvbeTet1V80m2QbPpk8tXsQIGmiaYnCgQ29pJoeGRtmFkA4m0C0XCUpnGtWDOELAwXyGXrbV0f6FUbnClJ3n/nSnuPbqTes2lWHSIxIOEwhbvfbzAtbfv4IUpn8tSSsR+Mw3sgiM5lVcNsisrQFVX8siompcCuliysV1rtAFK6/rxMdXPMBJTY+aFKclzU/CHu1brUe/vuPAYbmc7C/ka+Vydeq2BLpSWqd+iqWkCGi0+cbWrg30pf+n3tZ8zp+bx6CMnmCt52C0lElDz/LNPneHg1f24vobvQ7u1dH6uwmuvjnPPkZ3oukY+XyMS0glago+PT7M4No6e6Fu1TjQ9n5l8nblCHQ2fLZ3hLx2ivowvox3JkEnObmIlbZq+xHU8QpaOIZT9pd8S7F4pX/Vpo+mrck0qqK1rLvIk5EoNdMtkcbHC6TOLlIqOAo6oySQcNuhqZa4uJXRdZ3GxtgRMAfr642QWV7dfrqVCZDJVOrsiZDM1PE+i64JCoY4UAtdt+0xDqVgnZC+DU9hce/bdRUmxqcB+KgBpe3nCjQcEW2OqA329k5ICQo6nTAhO5iTX9YiWWPV6YLqyccvxfEwdbkgLnmwB1D0tgLoyg+pLCMZsGr7E0gXGBoAwHleNUaDGgCclMxX1I7vDquQWMgR3D8Cb82AKyfOTqtO2DYKLDckrs0rbchl0Ln9XzlGfSQc3ziaujH0bLFB9YUEiIPnZWZ9vtvRiNQG53PpusLilHGsq5QbhsMXxD8c5eGiAl18c5cabh/n+H1/J4kKFcsmhuydKd08EK6CvcAlSgv3X9ogljdFkQJBsgc13WlzZuwfXNyOdykuyddiWUI5XF4rPC1CllFQ9uKsLXplV7joblX2/iJBS4niSEznJ6byqChybVdzqugdXd6oGyICurokvJb6UlBswW4M35yQJCx4YURnuZi3K7HSBaCzI7EwJTYdQ2ELXNYp5h/mFMpomOHhoADtk8KP/9gZ2yOTqQwN8cibDa6+OE40GiMWDjI/liEYtKpUmH300y003byGXq+J5PqdPLoCQ3HjLCIV8nXKpwdBQgkDAZGwsy57Lurnrnp0889RpTp5YYPeeLjQnirt4Dh7/f6Ed/BPsQB9+KYCueQyHynjF97B3/wDPkzj1JrlcjYV5n1giSKnc4IVfneXIfXuYmiiyc2cXp082WFyoMDySYCCt43yK695+5vsjGi/P+CzWJB0tAKgB1YqLEALXU02zCIGuwZuvj3PNdUM88+QZ0h02sYTN5GwJ3/W4//49pNMGVk1J2R3oVM1BG4Vtqnlod2LVaODxMbXxbFeEPsgovVFlKbwceUfy3JTK7OcdNSdZmuDuQcHb8z5Pjkm2J7ikcbuSOz80ohzQvJYMgW5oS02r7ccon60S7oXZqlxlkdquJlVa/KFVSjRSzSmxpI0dMimWnLZvACBZmC/xzFOnOXr/blJJm5NvvsfC5AIS8D0fy1cb/AGSfDJfYa5Qx/clGj62qV2yffhvU3wJTr+MzxVDaZusYRGwNJyGR7XhEQ4oWSlbB1/Xl+SrPks4nqBcdpkZLVAv1UinbHp6o9i2ji8lkY4ILzx9hisO9vP0Y6dYqmwIsIIm9z5wGTVN51If3WDAIhJZdvfRNEGxUGP33tW6Q23OaTvS6RDjozn01glICYlEkDNnlGA3rWxrIh5kYb686libgfcHtgjO5JUV6em8svP7ypbl7xyJwa8m13/u2CyczCkNzIAON/aIpezESmAaNcQ6pxcrGeXabp1Uq8y2EqAeXwFQG55ExCNIXQfpYZr6EngAVeaLRANUyg0sS6ezJ8oj532SAcWxfG1OsjuplAkMTTUVLNbg/YzStLyjJaT95LgS0N4oGwoK0F3ZKXhrQYGczcqApgZvz8MNvevf+/pWjZ+d9cnWJamgAt7J5HoeqakJIqbSs8xlq1x5sJ+33pzgqqv7ySzWmJ4u0dcXY8vWFLKl9WNY2gqemGBPSsmRGUIpM0yWlduUlMpe9htbNwem1/XAewsKuF2sa/nzANT3FyWVpirt39YveGFKZc6+2AyqUpqoNZVKwJaY4IYeiaEJ9qVV9nuyIpmuQKfuM7FijHb3RInbOlGLdY5lAVsnGrOp1RyGtyRZmCtTr7vUHRcraJBK24RDAXzpUcirBb9Wa/Lyi+e55fZtCCCfrxEOW3T3RCnka7zw3FlmZ8oszFW48eYRfvnT4ySTQXRD4+TH8zSbPnfevZ3nf3UW09CIxoPs3NnJT378vpKfCioJP7vjVgLDVy2da9zIIAwL6TaQroObHsKrlZldlLz04nk6OkLUak3GzudoNj2+9d0DvPXmOJalqypN0SHdEeKJx05x1z07icYtfOSqDfzaWOJDzpXJZaok0yG+1h/hyVnBvUOSkC7IZxxGz2exAjqmqSsXKyHRdY2FhQpvvDbOkft3Y1k6hUKNA2usorttpXpxobA0QcJiFQ/8zTk1xrbFBXlH8tS4pClZatQEqLo+Z/LKHODIkOBXk5LjObhiRTXhVB5u7xc8Ma7mmIuN/ZXc+UK+xvYdHUs0Mb+luuGi5LuEUHP9VFWyYw19ol1NcqVSBajXXXxPYhgC09SXKjL3f3Uvf/dX71Kvt2kDGsGAweGju3n92ASHrulncbaAlL5y8hPglRZIJi5jfLHKTK6GwCca0OlLhD5X38hvcvzeg1MhBKZp/k7e3H+KMDRBd9Lkm1+/jJ//4jj1NkC1DMJBg+vv3vWZXXI0oJmr8fKjJ6nUXRotaqlh6Nxz326mdZuqq3P1dcN89NYYR+/fTbnkUCjUSSRttm1PE0pYPDKqJIMu7qoiCAYDRKMscQg1XTA7W+bm27YRDBrU625LcJslSRcl/RJhcqJAOGIh2o0SYYtCrk4qHVriZbUBWzva3ckbLSZRU6wqNz94XgHLm1qSL20OZbkpVzmSXNujBLPPFiS39i+X7UtNyWxd56vbdfA95qYrPPFYqylFQs2TaIbOfQ/sRusNMdLiGLYB6mUpJaL/+hxcloLjFZ1vfW3P0sQeiwdVt35A5/CRXRz/aHapycW2de4fWf4tDU/Zsb42C5auZJEMTZX5PsnBj8/4DEQUV7TYWM8XBfgkr0DUUBTqnirP3z24cVl/e0JwAettdiQEb81L7hlSFp1dPVEClrEuS2lIxalruj5PPHaK+76yh3rNxfd8unvCxBMhNF0QCFlohlg17ksNyfuLSupsoqx0VQcjCgxnHcmRYbGuNLcSmI4VlULBlkvkfq4EqDf1XlyGpx37OwS7kipr9fKMcut5eeaLzKBK3pxT/Mh0UGlOLltQLsdQWGCWqvz3v1bd4m2QsdIFbu284kqJGwsTam0qt25LoemCcsmhUnGRUt1fx/GolBr8L//mOqYmCxQKdXRd0NUZprMzxIvPn+f4h7PsO9BLLltHCHAcl9deHeOue3dgWTqlokMobNHTF6VYqLNzZydCU/PTs0+fWQKmh+/bTSRm0QhexcVCF4KIU+Oee3ewsFhhYb7Ctu0ddHSGeeetCU6dXOSeI7t47dUx7JBJf3+cn/3kQ3bv6WJrIE25WKfecEkmQ+vm3XaG8PFHTlCpNHBdBZzCYYu7ju7hbNFmh+Xy6kvnCdgmY+ezHD66iycePYXTcJfk0YolBztkMrw10cpkf3qraFD9EJUVSlofZRU3/slxn2oT7hxctgL1pOSdeSg3YTAq+P4OZdYhUXOJ50t0TTVL+lJRn/rCanPTfwHVqrWmEAvzFa6/cYSgbVJvcU6lVJsm14eIbdI/EKfakDwzIdmVhK0xaFeTUikbw5eUiq3MplDgUhOCWDyIbZsEgzo/+OFBzp/LksvWiMeD2CGTE8dnufrQAB9+MIsW60K6HmarohUIGvT1hjldaCKQmLrg6pH45j/sdyB+78GpZVkMDm5gF/NlXHJIJN3dQb73h1cwMVkkn6/T1x2htzfKc4saI5/xuCt3tKYmMDWlVec1PF566iQ33r+fN8saTcvm0G07KCyUkZbBzsu66EyHMAMqa9URVBPLxfrBDMPAMjSazSr3Ht3Fk4+dotlUnejvvjPJt793gJ/95EMqVQUuPU8Sjlh881v7ePvNcTzPp1Ju0NUd4sabt/LKy+cBpQLQ2R3mxpu2cPyj2aXvay+ylwbeBXf0w1MTyyDD0pe70/ell/9SQzBT9rmznyWusQaYdQ9tMstL71bYtbuTxx4+geOoRUczlCWiKT0eeugkP/zBAYIhfQmgPjEhOTyoxJ/fnJf84yeKKpBOrm5siceVzunCfJmrru5fyqi4UrlL+Uj6I4LJsuKKDURUY9Db85KqCxFTmREcHoYXJiEVZEObw4avZI+2RNXEvzupyu4/PiO5a1CBntAKkHoiK/n+zs2B1f60MgPI1CXpoMAOGdz/1ctX+7/7kobjct9X9vDs058wP1fmL3/0JrFYgCsP9pPqCDExnsU0dfbs717VgFdwJM9OSu4eUDJY6wH0+nNbC0zHy8qHvPop2rO3xQVnCz51TxC8xH5PTQhsQzmTvbOguIE39cGzE2qxbTvffLZQwDRktvUmNz+WU/P41eOnEJ5HyYOoqTLAjYa3TgECFI3opWnoCfv0AJFIAMdp4rU2kukOm2qlAQgMXbTuj8/cfIn+3hi9fVFOfrxAw3G57oZhypUGtVqDP/xnV5LP1ZmeKhIOW5imTiRqcfyjOaqVBvd9ZS/Tk3l27+1mYb5CNlNjaDjJvv29RKIWXd3hJZeni4UnJaFIgNGzWUxD58AVfeTzNc6fzdLZFaWvP8Frr45RKjl87w+vZGI8C0BmsUpPb5RKtYnn+jz71Bmuu2GYVKcNmoauQa3s8siDH7O4UFnVvFguNXjykY/5g+8eYGK8wOhojqsPDTI3W+bYK2Pcc2QntaqSTEulbKKxIIap+KCu51P1VjvRXWp0BlVDFgjOFdUMWHOV5FPHEsdUMlpU43BfWnCoW1t6/a15RYEZK8FcDfrCSpfVbqGavrBgrKiqFLpQtKi1WdR13HkpeeXNKb7zvQP85O/fp1ZfRs+2bfLt7x0gFDG4HNXd/3EOekJKpk5KSUdXBKfuKkqAUNQzDTXv1esu8aRNpdTgjdfH2bO3m3DYYnGxAgIu39+HYWpKd1fY1DTlbBiwNG66Zzfvli1imjqfZOh3r4y/Nn7vwemX8cWERBKLCJyQhh2OQtjCtHWk9Jko86kF+DeyOfRRANOTsFj2KGfK9MbiXNMtCeg6/akEZVdNmrq17OzxabpXK6Um6Y4wz//qLPcc2Um10gQknd0REskgf/QnVzE3WyabrZJKhejuiRAI6PT0xjh4SCceD9LVHSaZtLl8Xy/Dw3WGhpN0t3QQA0F9VXfyp8kqe1JlERu+XOKefpxVIG9tlFyWsnAWMDlV4e//WpWSYrEAuq4xNVkklVaUgkK2RjIRREPDa3rMzpbYsk2R70diyjesnUE92AnbYqqZZ6PGFoBkp706oyKUJJQmBBMl2JtUXtflpuRkXvEwU0E1kf/dGUncElzfB69My1UNW+14YkzxTLUVmcbesODOQVUqf2ZC8v2dy1qitgEnsqvLf2vjniHBP5yRfHu7Ao+rO8pVc5fv+ZTLDebnlqkZxaLDC8+dI5EMEggY3HXvjnU0jXgAGj48NCr54Z6LA5WNgOlNfcrL+5kJyeWfosQuWW/XeKlxRQf8alLRHe4cFDw+qnh+l5q9XXsmq4Hp5rHy+VdjXVJqrgSoLtMzJezeOAFNEjaVU9ZwVNFd/IZFZrGK29RBCFzXQ9fcljSQoFpvogHFYp1du7p49KGPSSZt7rxnB4VCnanJAh1dIQaHOqhVXUqlBt29UaIRE8+TPPrQSYqFOoeP7kL6Pq+8NE42W+PQtUMITUk/hcIWiXiQeq3JuU9ql2w7apiCRNLmmSdPU6+77Nvfw/4repmZLjI7W+KW27a2dFUFP/2H9wFId4SYnS3RbHhomsb2HR08/ODHfPu7B5iaLFAuOcQSNgvzlSW5uHb4UrIwX6GYrzM5kaeQrxMKmwQCBvNzZR7+5cfE4gFisSDTUwV0Q+MPvrOfsaLP2/OSQ92C6AZYSSJxfUWp8eT6pqvesODjnOT5KZ9cHb62VSw1d1ZcyccZyUwVBiKCB7aIdY2SSo9XkA4ub9ifGlcmI6CqOx9l4WxB6VuL/PoKwkpTiJonqbtQPpcjagr++IcHmZ8vk8/V6OgKk+yOU7cMXNQYjFpilcucEIJCocY9R3byzBOncOquypwClmVwx13bGTuf49y5DFde1c8//vgD7LBJKGxRqTRwHI9vfPMybr1zO57nM5tr0NtpM9QfxbR1nj3hM2x4WMBCqUHFcQkHfnch3O/uL7vEcByH6elp+vr6CAR+t3TC/qlDCEFPzOLMfJVqw6M/GSRmKTmigcjFuT8rYyObQw2xRLDPOJJivsaNu5MYYnmyjRifntt6vigJGtAVcJmcKFCvN7nn8E7+7q/fw7I0BoYSbN2WolZ1mZ8vowno7AwTCpnMTJc4cXyOYqGObmhkM2USySA//elHCOCewzsJRAO8XtC5c0CsAnGfthQ2WlLZwaYPlqYmfwFLMkjtJodma5JtH7tUdpeAKagGkUJBNXxlszU6O8MIlE5pNGFjalDO19BWNGltiam799SEasBKtcptNVeSdVTpbGVD19rmLg3VXQ2qixdUs9uxGdUA0V6UjmclB9KKdxkzl12r2mPnTF5RG7pDsC8Fby6sXmyGIoIeW3Iyt/ocbh8QPDYqkUJwdZfYkJcX1AW39qnfeLi/ST6XI51OMbg1yTZD481XJ3nqiVMcfWDPhtJRbtMnFNbo6YttQNMQfHu7yuy2rWltQ2WFrVWLruSDjCp3rgWmGkocP2KqTOzFdG5BlUpV6fTSy/orQxOCW/rVJzUEN/cp17KuEBtuGjaPSwem6ntXP/9tgFpsgBCqdDsxV6OoJyg2FEf8ULdYyvQbloYdMqlVm0SjAXRdUKs2KVeaaALiCXuJR9louNxx13YaDY9PTi8yPJKkpzdGJlvBbUpCYZMtW5PkMlWmp4rMzVXYt7+XcMQiEDT41TOfcOS+Xbzy8igfvPcGI1tTRKMWhXyd/Vf08ebr4+y5rPuSbUd9oK8vwv1f3cv0VJFspsonZxYZGkrQ2RVG08HQYXa2TLHoYNsGvX0xZmaKynFKQtDWMQ2N4x/O8uabk3R1R+jrjdFseuiG6hJvPx9CgPAl83MlUmlVA3/92PhS9chxXIoFh0q5QWdnmKMP7EGzDd4973N4eHM+eM6BF6YkwxFw0bi+Z/1zd+eA4JFRSTywrDpyJq/E9Pd3CA51w8bjdqVRxAruf1AsVcg0Idi/oqL0yKjPdEXpN7djpSmE67hYlkoAjJ7PMXo+R19/jHuP7gJDUKrVefyjWQiluG+rue750wTMzZT58FSGw0d3tRrlHOJJG9PUee3VMYaGkwghmJosLCU/KpUGsUSQYNjijTcnKeVrHLphGGNbHx+Ufba1+LuHugVPnZLsi6n5483RIomQwZYOm7j9u5dJ/b0Hp7A5efzL+PTREw8wmatTbfqcmStzZWeY1+bUBHRN9+buTGvjQjaHjVZaqrtDAanPe/tCJoyX4FxeoGeqvPfOLDfdOMi/+l+vZWqyQGaxSq3m0tsfJWibLCyUqVYa6EaAkS3JpcaAjo4IiZTN5ESeG28apqcnSihq8dqiUJqrKL7bhSS15muSsLnxwr8vBXNVlriTAsFIVPk4d2rLjU2BmM1tXVGCmoZuapxtAe52VCuNJeF46UsaDQ/bNqnVmriuRyBgbNikNRITnMwpt5Obe9Vk/P6iknrp33KpQEVyIgdzFZirKU5se1Gaq0rOFSVHhtT/j5bgig6xalOTdRQn8mCn4KVpn5oLz036DETFUnOMpatsynsLyw1Qvbbg+yM+J0fzvDlaoyu9ceZ6JCYYiYHrKjAEtKRbJJGYhZTrF+52hMMmd929g2g0QK2x2pYS1GL5re0qsztVkZwvwnsLStt1KKoyq89NKs3G63tgdA0wbcfepGoIuZA7GCjwX2kqXdW8s7HBwaXESvAcswQHu5Qjzte3sqG98fr4dMAUNn7+LU2gm3JJnD6WsKkItRELm8otrC/csp8F4skgpqGrMrYvSadtEimbQq5Oo+Fi2xau61EsOMQTNrFYgKmpIidOzBONBIjGgrz3zhTXXDfEqy+d5/J9fcTiNo2GR8A28H3Jc8+epVioc+CqPm65fRulQp1m08O0dIaGk7z26hiZxQo33brloioL7ZisSLpsn2TcwjCSS1zuTLZKd3eEgKUxO1PmycdOY9sG3/3DK3nvnUmuOjTIsZfH6OuPk9JDNBo+uVydYMBASIi2XCk818cwNPQVrAopJZapY4dMAkGVMX3t1bElAFUo1EmnbUa2pnjikZPccfcOrglqNOoBwpGNqz+pgOCuQUG26CIKRd57c7mhrf3c+a2y91BEURremldC+keG1zcGXkqsbJ5aG00fOjZYUlaaQmQq7pJblmXpXH3NIGgS31fNlncOQNFUMn439K6muPgSrKhNOVfj4V9+TDQaIJUOMTmRZ3KyCMC+/b1Mf1yguyuqMtKxAD29McZmypTKDlIqikQ8YnF5t+RnJUnBUTqwl6cEwV1hjo0JotInbXrkqx7vTZS4YjD6OwdQvwSnX8YXGrom2NoZ4vh0mYWiw2DK5rZ+g1N51Wl879D6ycOXct1EtJnNoURxE9MRg8H+z64CsDLa3aX1pmSiFMLUBW8em+TVF8bYuauTLduSBAI6M1NFhCYI2QaWqdNwPHK5KukOm1Q6xKIdJiskga097EosZwkczyfXUJOXqW+ctWuHBvzkjHKAigaWQepMVTJRkgS01YDgln7B+FSVv33yJML18aTKLnVGdY7et4eBwRiLC6tlsIpFZ6ls5zgurusTsHRqtSa+JxHG5k1aV3cJTuclvzin3rMN6LHVnblYZu69BclMVXE693coebB2NFt+8Tf3Lnvdd9lKp3XHigVAQykQjJcknxTgj3YJRktKr3Hteb41r7rMdSGWpGLqDZeCo5qR7ICxSWPNBmPU9+kfiGPb5oYLdzIZZNfuLvL5Gq5caUWlxqtycVGlyeGYxPUFfSHQhORsAd6cV17gl6dU9m+0KDcEpgDFpupyv9j1NjVVdnx9TtEBvqjYnxZ8tCj58RnJFZ2wO3EhkPrpgSls/vy3S8OWpbNjOMoHE3JJ4svUVBXkspY2sCclmqUTjQWoVJo889QnFAp1evtj7DvQS7ns4NSb2LaJEFAs1EmnQuiaILOoJKPuuGsH2VyFVDrCU0+eIpEMcvMt2xg9n2Vqssi+/b3YIZMXnzvH7EyJka1JrrlumCcfO0mp6Czx0gcG4pdU0gcFwienqlTzFXZtT9LdHaGrO0yl3KBYrGOayg3r5lu30Nsb4713J9mxswtdg+mpInv2dhMIaFTKDvF4kNMnG9TrTW68eWTpmZdSIlbcs4Bt0tXSZz18dBdPPHZquaQfUyDr8v3dPPX4KcbG8vz4b9/lBz+8msf+8YNNm9M0wM3VeOIXJxC+R0Bbvnftzzw/LUkE4WReKZLsSYnPSBm5eFyeEktVp5VxIVOIjWhXW2KCTAMmS5Iee1mnVUpJvCuKril+erHk4Hn+kpRgMGgQCptMTxU5eGiAQMCgWHSQskgoatHwIGqubJJVNssrn/PtKZORRIyfnpWMdLpMZ8o0XY/3J0pcNRwj8jtU5v/dshT4Mn4jIh0xlx6nt0fzTOVq2Pr6Um87nplQMkBro72jtawVTQ++Km3ffec2SvkqzZr3hQ1iQ8BAXwRTEywuVMjlarz+2jg/+8mHZDI1XnjuHOVSg9mZMlNTRfK5GrlsnccfPYXr+rjnZvAmF+kVTRo1l/GzWd5/a5reaoHDnT5nR0u89+Y0E+dym553e255Z1Hys08kizVJzZW8OadA3W0Dqydur+7x6tMnqdY9ik3VMGZo4DZ8BcbqHh2d69tV29m/QMDAMLQlm9ZI2ODOw0phYaPIOpJYAI4OC/amBEeGBLcNaFxKydjQle7hNd3LtIB2fJxV4H2lVmk6qKSbsvXlsTEUU1aEbY/tpq/khNaKdH+YkQxFFNVhZWOdMomAigtOw+WJR0/i1NZ7vG8UobDBd/7wCoJBc2nhfvP1cRYXSgwOJXjxhbN0dIZpNpeXs5kK/MMZdS//8oTP63OqwaM/okT4R2Iatw8IvrlN4/4RjZGY4PwFgOl4WXI8Kxm8iDPYyrA0uMSfeMkhNHXtTU2pLjwz4XOuKNfwGT8bMG3HRs8/LIObcEjn/hFBxYWgoQD4iSxMtGzd56qSt3MgdI14PMgtt2/lmusGSSZs6rUmnZ1hRrYksUMmpqlTKNQ5eWIBBOzc1UlXdxjT0tE0jWqlwa7dXdxwwxbKZcU/ziyUefP1cR558GPm58r09EU5ePUgL/zqE8qlBr4vsUOqiSYgy1huZYNfuRyfFCQPnvdp1lxeeuokb786Rsg2qNVcsotVmg2JZSle+/YdHSBgZrbIlVcNEI1aPP7IKYJBg2jMwtAVqA+FTUolR5XlKw5H7lPPvJTLT2wgaHC0dZ1npku88do49x7ZyV337ODqawY5dO0QBw8NUK00OH8uB0Ct5jI/VyYaC2z6DLWfO9/1WLG/WGpoq9U8Jsvg+oIbegRHR7RfGzAFpcixGRVlJXf+wNV9hPsSTDc1ZqobL1rX9AjqHjw/vfr9/pTBvUd2YVk6fitVbhgawaDBPUd28fqxcWDN/Guqp1wAwtS5+76V8+8GG2VUw2cVk4MjCcJBE18KxjP1z3BVfnPjdwdmfxm/MaEJQTJskq00EcC5+QpXDBtsiW083A50Cp6akNwxsLqcvXJHOz9bYn6+CgKCIYsXXx2nUqgR2jT7denh+kpyJG1JAobBtdcP8/gjJ5dKtsrOcJbb79zOL36muKShsEW10qDR8LjnyC5c1+OlF89TrTSJx4PcfOsWPj4+x/xiDd/zadRd7jq8k/dOZljI1IgElQzT2vMuNpRG33BMEflfmpYcHRFk6iqLOFES3DqgOJjtppFmwydsKNF9TyowAGoRGBvNMjAYJxg0V5X229m/w/fvImRbjJ7Pkkja7NrdyZslnc30K3pCgp+dlfxwj6B7A3mnC13j+arctOzWH4aGt/693UnBR9nlEna3LcjUJOeKcGOvoOmx4Sx2sFNRSXanYHFNY52pKRmjnAMR371ke10PGOwL86/+7FomxvNLfugdnWHeeG2Mqw8NEY0YtIXCqq7kiXF1zK1xyDuCThvKDcnzOQB1vyKGoiv0hQWGUEByM2B6Nq84u30XkMdZG4v11dnnzxulhqTZYjPsSsCuhEbVVTzfZyckBzpU+fStzwFM4dIyWomA4OgwPDYmiViKq5tzJJau7G5HonCmaXF5xKNa9ejtixFPBAm0lDzOT9d45dlz1Io1evuiXHZ5D7Vak8mJPImUTTBg4rkevf0x0ukQzzx1hsWFMnfdu5N7ju5ifq5MqeiQSofo7ApjWTo337qVhYUKiYRNV7dqmJyaq7G1Mw+xzW/c9rhgR0Jj4lxxKdX95uuT7L+yF8sKUa02yOcb6JqO0KAjHaLR8HAaHi8+d5bR8zkOH92FbVu89/ZECwyNIaVSK3jx+fPccvtW7r1vF/Vak1JRZVZj8SCdXWF0XXDkvt08/uhJHn/kJKGwRShkkskYXHVwgOd/dW7V+WYzVdVtvlBZ9wwJIZibK9NoegQM0RLKV+FKSanicma8xDU9cXbGP9/YPJX3ma9CMijYk1huBJ2tShwPhi9hI5epS45noen7VF0FCbtsmK+BrkmODK1PJczXWHKla4cvwbI07jmyi3JJuQV2dIap1ZQMWbuRsj3/Hrl/txLlL9TQI2psf1zRqJTkpuft+ZCrqznR0DUSIZNy3cX5IssjvwHxucDpn//5n/M//sf/4NSpU4BKa//FX/wF//k//2f+/b//93zve9/7Qk7y1xmmadLf349p/m7xNf5nx96+CGOZGhNZtZs7OVPmiqE4G+0Eu2z16kPnJP0RyZUdqhMSlne0PX0xPvxwlolFB+E08KTKflWaLj998AR/9IcHsEOXqJWzJjypOHmzNZOObJZXXx5dVbJNJIJs3Zbm1VdGuevuHZRKDvl8fUmf7p23Jjn6wB4iEQvpw8JChccePsF9X9nL/HPnKOTraELyzJOnOHx0Ny+/eA7kxnI4/WHBY2M+z01BT1hlhJ6dkFzXrXb+/3hW8sGi4ECHylxNzddo+GDp0PCV7eTKaTSfq9G3Jcn3f3Alf7eiKQqgVHJIp0I0mk3q9SYjW/qxIwbNvOJybtToELME9mdoOjM0sSSsv1G8tyi5umv9+zVXSbWsjJt6Bafykns3WDDa0WELbu2HdzMQWdNYR8sgwtIleQdG52r0bU2uum66bpBOp9H11VNkHYjHTKztaTo6w8y2QNPtd25HN6BS85aA02uzSru0J7T2dylaAyw3e7m+ZKGmSvYH45sD0ys74djcp+GPSqYqih/8RUSpIXlpRtIbVhn6tq1tyFCavOUmvLeoNlK7k+IzA9N2tJ//c4E4t1+9cSNhKii4b0TxYKOW0meNliR7kzBTFdzUJ9HQ6QxFl5zQ6q7H352W3NAb4Otf272kyAAwvCWJ5/qUWi4+/QMJMosVxs5nufJgP5Wyo3isns/WbWkK+RrFkkO55JBI2hTyNWqVJmPns9x861bOnFognrCp1MEOVWgYmwPUtY1g778/y/4reluSb5J0Kozn+USjFkJoTE0VGD29wO693dxy2zaEJpmfq3DnPbt48BcfLVF6hBAsLFR48blzXHf9MN29URIJNX+lO8JLmbot25L88Q8PMjdbplioE4sHEQJefvH8KoUKgFQ6xAfvz+B5PvlcnWEBPgJdQLPhMz9TolppYhgaEV2j4UO9VSELGUClxp5kcpXk2qeNibLKzt87JJiuKC5od0htUDwf9qUvbfyZGuxKQsISSwofEiV/15amWjkntIsjyTXPoZSSWMLm4QdP4DY9+gdiDI8kVAm/sDqzWak0SCZtBoZjeFLyxLjPtghcF4FHRiX94Y3pMrYh2JFQVZmRGK0kkE/I+t3KNX7mX3PixAkefPDBVa/96Z/+qbo5sdjnPrF/qtA07csu/V9DGJpgW2eIhuszV2xQb8CxT7Ic2pLEXlOmEwiODEO5ocDIy7OS/WmWLB6FEMxMF5meLlFrKu03EzA0ZW3acDzOT5TYsj3BJtXoC0ZAV2AhZBk8d7KyimsVCluMjfqcOb3AgSv7ef5XnyA0DcvSOX1yOXM6NZlnYDDO8Q/mkL6kVncpFesEg3qrGQqcukul5BCJKCH+RsPbMGt3fY/Ssmz//qor+TirnJP2JlVT0lhJsjWu05u2ea/hUnZ9LEPDtHRqniCgSXShJGlemPK5qz/Mv/6/XcfkRIHFxQodHWH6B2K4noeTc7n9ru1LoKo3LJirqolvo4iYCqS0NxBfRKyViQHV+PZJQWkZroxUEBwf/vKEzw/3rOZkrYz+iHJf0jZprNMRpAPQ02Hz9rzPoc7l4wghMIzlDasnJbNVdU8agBnUiMkAbtNncjLPyy8pTVvL0jl8VGXy7xi4OOGk3exlaILeMGxgYLUETG/tVzavBzs3/80r41xRif4PRRRP9/NGG5iGDCUNty2ulDhemIJb+xUHNmIKburlks7vQlFsqExswpLMVAWFhrwgiEkGBJelVEZ9IKysTacrgpv7l4H+ymbEibICJCNRKDgai9EEfkTwRsnnNkOn04Z6vcn5sxm6eqI0Gh6NpodEMjySRNMF9arLJ2cWW6YNEeygyej5LJ4HqbTN/it6eemFc/QPJLACOhknyVB14YLZ040awV47Ns7ey3oolRyCAYNGw6PW4so2HI/OzjDhSIBavcnbb04yOZFnz55O7rpnJz/+2/cUZad1O8rlBsl0iN7+CFKwDuxrlka8wybVFcKte/z3/+tt5mZLqyTboG08EmZxvoymC5Ipm0bdI7NYwXclx14ZY8vWFOWyo5QeNI1IPIhtaLQlUT+PrTUoJYrRouTr21S5vtNW8lGFhtpESwk/O3dpVYOYJWhPd1Kq5+Z0AbbF4GCXeo5XzgmWUNWNvz4l+e6O1Rv5UEjna9+4jL/9q/c4eXyehbkK9x7dyb/402uYnSmRzVRJd4bp6AjjS5+mr9Qmyg31DBma2uj93WnJQETSGxbUXFqGLOp79neojczoYpWq46ILGE5fxJbrtyw+Ezj1fZ//8B/+A3/yJ3/Cf/kv/2Xp9SuuuIJ/82/+DXfeeednOhnl3LHaIVjTNEzTREpJo9FY95k2sGw0GuvKcoZhoOs6nufhuqtlX9rHbTabLCwsEI/HMYzly2FZyumn2WyuI7Lruo5hGBseVwiBZanujLW/BVSmVtM0XNfF81bzdNrH9X2fZnN9t2/7t/66jnuha7jRcS92byzLYndPGAOf0UwNhMa5uSK7eiNomuJy+b6P77e8iHX1zx298OaizvuLEkM22Ro3yMxXaXpyRQZI7dBjpqThg1OsYmvRlnOTrhYib61auVi6x667/jp4uka6YzlNVyw6FIsOpqnRbHi89uoYX/+DfUxOFshmlp09Xnt1jJtu2YLjeLgruLOFfB07aLKy1ytfqBO0DcpldQ9z2RrD25J4Ui6Npaiu/nFddf1DhmDQdtGk5HRGeYkPxnRStRr5WpNatYnTUJ/VhFooar5GKqyR6IoiMk0cV6AHodmd5IbLu9A0ietKpPQJRQ186dNolYWGIxovzAhipk/MXFsqEtzap3NsVnJH33o1eF03EELg+96650bTtFX3xvUkHmpzkDQlZ/Imh7rBdV3yjuJnXt0pEL5AiuXjhjSf6zokx+bA9XR0IRCaBshVz6MG9AVBthpr2tdoZViWzlB/lNfHPK5MLt87z/Oo1WrEYjE0TcNrNnnqvOQHu9VC5TUEP/+HD2g0fHxf4roerqtE+h975ATf/t5+TFs9c67n8nFWlZw1oZo/vr7dJBEQG45DdQ3B83zOFzzGSnBTL/ieIKwLig2NziAbjG+WFs+5coMXxiVXdQouSwukB/JT3Ju1UfMNBUyFS18QhsMC14WdMZBS47lJwc29Psg1rWVCQ9f1dfdm+XwNQOB5HrL12aIjeW1ecqDD4OOcIO94XJ70cN2VYF89y76ULFSaRAyYKUtMCYU69NoGvWGB9Hxc6eNLJbvW9OFEXuNUQePOfsl7cw0+yiidXYD7BgWWqbr9DVMnl68xPVnkwME+lf2sOJw7l8NtuMQTNp1dqlFpZrJIMGSSTkcI2g2aDY+HHzxOIV9n154uApZBLu/QGYvjVYv4ZnBVE2h7PvQ8l87uCKalLTm3nTyxyFVX9xMMRpifL1MpKWeqaDTAyNYEmtBYzFTJZqr09ceWxP91Q/Cv/uw6pqcKLC5W6egI0TcQIxjWqDcbF7w3TaAhAtz3lT38/V+/S2PFs2PbBt/67gFeeuEcmq4RDCoe7N/85dvcfud2nn36ExYXKuza04Vp6Crr63uUCzWS6RASCFgGXT0Rms3ldWOxJhkta1Q8DRPJ9d3eOufG9vjOVZt8MOfzzW0CHTUWdd3A0ARJS41vAaRNn/fnBJd36BcZh+q4b8w0OJ5R88BIFC6LC3zfaK2tTYrFEuFwCF3X2RKGE5bkRM7kqs7Vx013BfiTf3k15z9ZJLNYZWZKZZGzuSo7d3Xx2ttTvP7qKN/63hV4nofve0Q0n3JNEAsI+mzBH+82GC9JyvUmfcG2gogKXTco1VzGFsr4XpO+VADhuziO+xuPIy41PhM4/fGPf0wgEOCBBx5YBU7/7M/+7LMcbilc12VqamrVa5FIhK6urg3fA9i6dSsACwsL6y52Z2cn0WiUcrlMJpNZ9Z5t2/T29uK6LqOjo6TT6VWl/eHhYXRdJ5PJUK1WV302nU4Tj8ep1WrMz8+ves+yLAYGBgCYnp5ed5MGBgawLItcLkepVFr1XiKRIJVK4TgOMzMzq97TdZ3h4WEAZmdn1w2c3t5ebNumWCySz+dXvReNRuns7NzwGgoh2LJlCwDz8/PrQGZXVxeRSIRKpbLuGoZCIXp6evB9f8N7MzIyonbMVMhmcuimhXDrdFgOkWiMcCiE4zgUi4VVnzNNk5t6lUDduckc1HSScZNa08MQIE0DgfI99qWPDiTiFplMlkAwSCQSpdlokMvn1lxDg44OVU/NZnNLiyFAwLIoZZv09ceWbErb0Wz6GKZOqVhncaHCG69NYIdMTp+cX6EzGOWFX53FaIudS4gngkxNFZYb2SXE4wFmpnPLQDRu4Uu14Vt7fQG6u5XKc7Na5OxUg4ABDQf6k2n+6u/eIxYPcPi+lsWg4+JLSS5bo28gyl1Hd/NOQbDdzNI+dNSHqSno6upE03Ty+QKOs7rcFIlEOJAO89DZOkc6ixteQ4kkk8kCq8d3Op3GMEwq5QrV2urnJhQKE41Gcd0m2WyWN+ck+Qbc1i+YycJlI+q35vN53p5tsjchoAqZKiQTSaxAgGq1SsNxKDds9jSrfPBWnXQ6RF9/AiMoNryGvd09HL5vN4889NEq6ScroHPz3bt4Yk5jb6xKJrP8PLquAqeRSAQhNLLZLGFH8tE4DMYMilnlVOS6kkK+hmxloKSEYsFhaqLI9l1pyo0G+Xye+YwSkW96YAiDX5xLA5K9xiJNX0lYFRswVpI07bRaBKpFwtTZmxS0h7LftHknF6XDbOCUVo9vTdPo7OxS/1PNczDsI6uSTCvbkkymsCyLarVKpbK6Oce2bWKxOJ7nrbuG1Sac8LoIGZDyisRdl5V/si2eIGZZvDhWZU+osopCEAgESCSS+L7c8N50dXWrTvliEcdxKDVUxurylCCpx7hz0GYs20B3iqu+0zRNUqk0NQ9ePJ3B1BS31bKUm1DNSDNbEeTzRWaLdaX9qStw6llhBmMRnjzfxG7mSJvgFFXlxqsa7B7oxAeassKey9PkM03Gx/KMjKRwmxLbNkj0xshkKpw/m6VvIMb2XZ28+doYr786TrFQp1h0CEcs7j2yi0DQIJ+vYYdMFismvR0uD53OoklJX1iQCEBXKkEwGKRWq9Fw6tx173bV0Oh4OL7GE4+e5tY7ttLTG6GRCtJo+Dh1D9f1yWaqJJOK+2xZOrZtYOgab7w+zk03b4W+DrYNNdHqRQqVDIVK+xpapFIpgHX3Rkp4tpDmD7eF+F/+9bVMTuTJ5WokEjbpzjAvv3COmZkSAjj6wF4e+sVx6nWXRsNbcqBaK7nmuj7NhkckanH4vt14vkOu9cxVm6qJcUdXiN2dMcKaSzabXXVOQmh0danx/c54jgO2z8opfuUc0R7fOwzIlCTlYIh4PI7nbTbP9tDwJadmihzuVaTTUznJTz+Eb16WwLZtqtUq8/PzRKNRDEOVIrSGydZYCinXX0PLNLh8fw+LC1XGzmcplxyicZunnzmFU3M4ckcvo2WHHl9QLRfYaUKtCE1NPTfpVIrhmGC2moOqJLNiOk2nO5jM1alVKzTrZcxQgPYS/JuOIy41PjU4XVxc5L/+1//KX//1X3/aj178ZAyD/v7+Va9pLQPzjd5bGZ2dnRuidVCLbTAY3PC4QgjS6TS9vb2ryvvt99PpNMlkctVn1Q5MTehrz2nlTq+vb70QYfucksnkOvpD+7iBQOCCv7Wnp2fda21gHYvFCIdXl43ax73YNezq6tr0GobD4U2voaZpGx63fS06OjpIZzSkprOtO0w6HmSyovHyuI/vmyTNFDsSrBA1Xr6GQz0KpHoJQSJiUal7S+9quoaGKrH3DSTQLbl0TqZlkU6vUGBec9xUavU9DVomr7wwztWH+vl2y7puJUB1mx7f+6MrefbpM5RLDvmc4oW1swilYp1CoU40GkBogkBAJxIL4jgeQlP6pgHbIBoNUq/7yirV0unriyOlOu/157scvR0J7kt4StdV0xg/XcBxXBYXXNVde1RxZPN5xZHdtiNNsCPE5GmXdCrJruTqEnP7OkWjUSKR8Lr3mk2QmkUwliK8yppQ/XfIgHQ6te482xzNcCSMHVpdZlp+lk3S6TS7Tclbc5IX89ATVdJKAPFEHKfgM9ijrTtuJBxmuqjx2IMnCaLGQt0T2EGdBx7YTWdnB0jlHOX7Unl/oxprvv39K5mfLZHL1UkkgwSTEU5UNR4YAenb+P6yHlWz2aRU0jF0DaEJOjrSXGdL3p2X7IobTI3NtYBoHSlVlrP95Pie5Pz5HH0DcYJ2gI6ONHd0rLwSgn84q7jWETtF0xe8UZQYAu7eIXCkgW0KTBHD95e9WyfLkgVHYOnwyrzBTd1r781ypFNJ1t6e9jUMhUKbPsu6rq8ah0VHcmJOEgqoUv5wOMHaDYmm6QwGNSpNm3dyFncPLWtUCtGeI8SG47s9XUaiUT4ohbF0yc3bBSFTqGyiEAwng/j+2p4A9cGwITi0NU2tKdnSGuPJlORnozpbDBCBMLfvCGOvuE5TVY26B4ODJiE9Td2FswWJGYJ4fHnMRaNRms0mnX0GiUSKd96a5MqDA5SKdSYncqTTIYYP9nPy5BzP/+osX/3G5QwMJlmYL5NI2qTTYYrFGq+8NLokHVTI1enoSPO1vTZTVcFUGU5UJW9O6HRFJNd12gQCAZwIfPv7VzI5XSaXq9PXaROPBWk2farVJlIKXOGTiths257i2afOMr9QRgiU2UCpwde+eTmmKdgWBs/T8K3ommu//FtX3puC4zNegt6QhmEIPOnT2x8nnrQxdI2FhQr9A3G27ehgaChBNlthYrzA9h1pMotV/Fatvt30c++RnVRb9qcDg3G2bk9hBXVc3yMdsHA8yak5CWHoTxrEAgIpzVXnpAmBpqkmUCklXjDJniElct22Yt1ofKeBwRW/Vdc3n2dfmpJ0JmN0tug9nR3ws7M+802dYRuCgSDRaJRUKrm03pbzkqAyIdvwuLoh6BuM0tkVYm62xPx8hd1XDrGnF+q/+L/Tc+f/gyfyu/n6FvVZTRM06zA/W2bi7DTJlI2dSpGMiqXrCjCaqZMpOYTsIDuGE3RFl+eu33QccanxqcHpf/pP/4lvfOMbbN++ncnJyU/78QuGEGJT/ueF3gOWSukbha7rSxd2o+OapkkgENjw+BdqlLrQcYELnq9hGKtoBCvjYjzYX9dxL3QNL3Tci90b0zQJBS0cT+BJHcMwGYnDSBxAY6IseS8nqblwoEOssjpd4vgYcPSBPTz00MklmxWBWJKVsUPGqiaJtZzB9b9n9XtC00inQ7z79hS7L+vmBz+8moX5MtlMdakTNxw2+do39zE5kWdhvkK6I0RnVwTHafLU42cAqNWadHWFuemWLbz/wQyaJojHgziOyz2Hd3L8o9lV5x1cOu8Ln6+u64RaY800NHKZytLCvrBQ4bFHThKKBIhFLM6eXcSXkht6hzk8rPHkhIluwu7Eeg6kGr/rx7DjS7YnNF6d17itf7ULTNZRVIoLna8rNUxD29AVrH1vdEMSsJQ7zEp+5oc5g+1JMDZoyGrWJc8+cQrf9TEtQb4BuoBSzeOhh0/yrW/vp5gpk8suC35rhmqsCYQMhralGBGqhHg271Nx1TgSmljebKHK9pW8wQcTcy2b2igjcZ2QJXl4DK6Kh6g3PHxfrgKm7YhFA5z7JMOOXR0YgdXXSSJJBCS3DQhMLQBI9nWqjmqBYBnSazi+RtCAqQqcK0tuHxScykPTF7y6ILilb2N5nAvdG8fXyDgaA+HN7w0ojunrGclIQhDQYVtccKElY3fK4NicxhOTqot5tXbx5uPbR3JsXmcooW8oJaQoB5vzd0cSFg1fomsKsn6Sg4Ndihv9yozBod7Vnx1elRNQY38osf647fPNZIr0dndx/U0jTE4UaDRcevtiRCIBxsdzBAMWX//mPrKZCkII9u3v5cMPpnnu2U8UJ/3enQSCBqVinUDAJJ+vEYsG6AzrrFR5+zAjOZ7TuKJT50xOciIr6QwnODCiEbdUZj4Y1EmmA/ie2hiNjxfwfZ/dl3XRV4ipDvxEkHRHGJBMThbYubdraQO8cah7U3ElD5+X6OgMROGmPoELJBIB6jUP6Zvk81UEkqGRJELAiy+exw4aqFEtl4T+2zE/V+aRh0+QSAQJhSy2bksRDJu4nk9Taryb0ZivSvalBdeFwWxNRe1xqKEkqaZbBiPdvRHicZs+p8Lxd1vPeG8UO2QoNzkpWxSJzdbl9ePwnXmf9zM+tgHf3mGuaEaU+JrkZB4+zPrc2SswDB3TNJeOEQ74PDUu+cpWbdPxrfR2NfqGEwyMJFgsVig9/P9EZM4TeP1H7Lzx/8Awwmgs6zE3GorfnG9AzNb5+ld2k+4KtYwLJNP5Aho+mq7TnQhjGevv7W8qjrjU+FTg9NixY7z77rs88sgjn+ZjX8aXgW3pSvszU2Uoba9auAYjCpCWmpIXpiQnc5LregTRFdkOHzCTIY5+cz9+ofyZ/ek3C9+T9PXHeezhkwDsO9CHEw8QDBqYpkYgYGCaOoapEQqZ9PRFCQRU9jMcMbnpli1kMlXS6RADg3EwNKqaQb1Yo7/TpqMzQqFYY+u2FImrP995ez6txWf1a9WSQ7XFZU2nQ3i+6mQ+PAQPnpc0PJ/96Ys36Tie5ExecmWnwPPhhWnJrX3LpH/Hg47gxhm7djw3pWR8diU3/ru8I3l+ShIw4PYVjUiuL5koSx7YwHGqLZ3lNT1cqZqiNMHSOHEdl7Mn53n/4wWEozhRKwW/fZabYpIBuLpbow0rfalkZ2xDLRCPP3qCatXBMIxVm4muLpvDQ5J8KYqma6qUv+Y822Lb83NlOjpDdPatNjQQqOanvzml3MBsU9AbgoOdy3/jtzRCa01lD5sIKPqDJgQDEaV7e1OP4PlJyW0Dn85K1NDgnXnJh/rGEjmggOnLs5Jb+wShT3Hsb26FX55X3cb3j3BRlx8fycvTMBjhc2lcns7DmZyk4qry/v5Wl/bHWTWeVm54P0s0XI9mwGAq3sGRLQb1mkc2WyEcCuD5infc0xelUfeYmyuh6zq337mdZCpEqVjnlZdGuf7GYeKJIDPTRWSPT3/QoiaWF/LdSSVPBLAnKdiTXAZJ7eEjpaThSfKLNZyaS6NF5clmqsQTQXp6o9RrTQqFGoauIaVqNlzPEFwfpoCvbFG2oLq23KznAoatk4iYzAdsdsYEx9+a5InHTxONBrjqoKKxLcyV6bgtvKG1b7Hg4DQ8UukQnu/zQUbNMfvTgmu7xYabWA1WgbVkysa2TX7ydx9iBQ0Cpnr+hIC7D+/CMAXJZOiS59XX51SjKcAf7xJLElTta/7qjASplACen4RnJyWHIquPcU234J15ydmCbG3eNo/23NNVPcfpc++SDIAz9h7brxvl5ZnLuDaxrMcMUG/1semezxOPnlpSdtGEIG6blGoNJJLXzuVJhkw6ohaJkIFtbp4w+22KT6Vf/tBDD5HJZLj99tu59tpr+cY3vgHAtddey6OPPvprOcFfd+i6Tjwev2AG9Mv4/LGzJYopgDfP5Sg764nRtg6dtuLePT2+Xpg/acEbeW1JKHlwaxLzCwCmGrA4V0XX4Vvf3c8rL43xX/7Pl3j/nWl27Eyze28nnV1BMpkqr706DgIGhxKEwyaaJigV6nT3Rth7ZT/xLZ3IoI4V0Ni6LUFzqJdiIsG5hk60O/qFnLfv+/QPxrGDa3bqrbnRtk36B5cdaZIBwZUdcDIHM5ULt8eWGpJnJyV7kqrzOh4QHOoSPDe1/LlKU3U6Xyhu7hOcLsCvJn3eW/CpuurzTV/yUVY1O13VKRiOCBZWKD1pQul+LtaUTMzKEnJbZqfUhJipAHl7PZFSKq5foY4ZMGm2SmBtwe/NhPY1lOj9z89JXp2VjGeUNFmj4a3KNJVqLg8+fIJazSNmCcq6zv1f2YNlGauq3MGgEuF+8/UJ4vEg2Ux1Qxmn4ajge8OSG/QCI4szBDJ5JjMep/Nq0fybU6or/rYBjTsGxBIwBeUiowtBw1eL48PnJR8s+jQ8iXuB9udyU/L0hM8T42qR7NnESvizAlOAWEDjK1ug7sLzUxceaxcDpk1fOYqtNGHYLC5PCe4cFHxli+DKFZudo8OCswVls/tZQtM0QqEwmqYxWYaI6VNxGni+RzBooutKkD6Xq1Ipu2SzVSTQ2xdjfDzPT3/8Pg//8mOuvX4IO2Ri6IJAwGBuroxorOHaa2JJoeOC5yQElqnTdD26uqP4rk+53KCYr7O4UOXdtycJBAzeeWeKzg5lCrFydQuzVlpNhaWr+21oG4PFt2c9Ppx3MYTEarmFFAv1Jbe5YrFBpbws9N8OAZimxpGju/GDFj//REnVfX2rYEdi4+8CVplnAFx2eQ9PPHaKaq1JuVjH83xy2RqLi1V+/tMPKRUa/PTHH5CZr10U2GQdBUyPDgt+uGctMIUzBSWLlgqqX3DbgKqsVLBXzQvdtuD2AcGL05KCc/ExZrkVSi/8iKCueLYA2ms/Iq3XGJssUa67NHzlKudLZRgCLCm7tKlyu/ui9CZD2AELT2pkKk1OzVZ4/VyBsczG9/e3LT5V5vTf/bt/x7/9t/926f9nZ2f5zne+w4MPPkg8Hv/CT+6fIgzDuCDX78v4YiIcMOiNB5gpODRdycdTJQ6OJJYmhaYvl+wWt8WU+PyrM5LL00rIO6ip7GndWy0J80WEU/N46flz7NnbRSRm8YMfHmRhvkI2U+XDD+bo7Ipg6Op78zmlYTg7XaJQqHP+bJbrbxohHDb56MQ81966nZfmBRIFvq7rgpkaOC5UGvIzSV1tFKGwwbe+d4B//Pv3qdWbCKGaGEIhk2999wChsMFKOHagQ1B1JcdmJdf3KLmojeJ0HoaigvSKzOhifbWQ9bmC5PYLaJaC8je/pVfy+pxyhnp1RlJsKHH0oA73DKlF6WROsliXbJMtXpkQXNMNr8xKMjW4d0hploKSvQnGbExNZf98WgwPAc2Ghy8liXiQufkS9RWnt5lkVzu2xGBLTEMIwcS5Il7To+pBQNfAYwno1qseJ0dLjNtxumxJMhrm6Ff2kM/VKBaWdW/feG2cYskhEg0QCOrr5HI0QCvW+EVr4S03JU0f0hGdu4/sJhgNcVkKIq2M8ErXLBWCLXHJWAmu6hR8cxuMluClmZa0midJB6E/ItgWU6L+5wvKdeqabkHXJqAUoNRUwPSWzwBM25EIaHx7h+TteTiVl6v8x9txMWDa8CXPT0o6bIFtQCrY7ircPGIbSJtpQnBbv+QvTyh72N2bZPI3C03TiUYVV/NMwac3BI+MwuWdOvFqjWbTJ5WyySxUKIs60ViQYFBZVCbiNnfcvYOe3iie54OEV14e5cabtzA/V6L89k8JXvODC+qebhRSSmrVBsGgSbFQp38wTqHgUMjV0A2Na64f4b13Jrnu+mGsoM78XJlYPIge0LDcCpW3f4J18Nuf6nuzdcmHWckf7hJ4vrKWDQQM6tUmrx8bbTU+neb5X53l9ju3c+T+3ZSKDpWyQzQeJBS2SKZt9IDBd3YJEBLfB68lo7Q22lWSNjANR0yKJYdaqw9A+lJlZ1uNpk7dpVxyMC19Q/1ogIWa5LU5SbauNrf3Dgm612kQq+rNRxnJ9T2CV2YkDV9iaYKbBwxemIoyPg0jMclIa9wGdUGnrQBl/GKqlNnz1MfeQwhVgTI8CWPvsS+wyIsZU/HnNYnuKV1b39CQppqb8rkawyKJJyFgaGzrUvcvW2kwkalRqDXRkJxfrBEO6HRENi+v/zbEpwKn8Xh8FQhtdxz39PTw5ptv8s//+T8HlJTAf/yP/5E///M/59ChQ/zFX/zFF3jKX2z4vk+j0cCyrAvymr6Mzx+7esL0JQK8M1bEaXqUHZe4rbaGH2ag5koOdSsw8vVtgumK5N0FiSsVMPGkypbVXLmhQPxnifYkKHXB5HSRsVdzXHPNILomSKZDICWLC2UCQYO3Xhtn774eKuUmuZwCI9fdMMy7b09y+107kE2fWqbE3WtA0MCnW3suKTygpy/Mv/jX1zA5UWBhoUq6I8TQYJxQZDUwbf1SdiWUOPUHGcXP2wikXNmlstadtsoKgMqStv/S89XGYCNd0tUh+fk5+PZ2QdgUXJ5aFpxfGbuS8Pa84KHzkjv6IRZQBgyHhwT/+Im/qiwspSSUjhCwdMp1j6bf9qwH15UEggaRaIBauYmxppy8cmJfH+pv25lZWxdYWktXUwjayVFfQq1Yw44m+DgracYF2xM2L784imkIPvlkkVLRIRA0OHxkF6dPLXDXvTvWLb5rM0JBA0IC/KbPc0+0yncXKc1NlqC3Na5MTbAjrmxc2ySD2YrSgXxvQW0GkkG4rlssAf2NouFLXpqW3Nq/uc3jpYfgyk7J352WRA1JX2R5br1UYHpV54XP99Ocy3d2wJPjkpGYAhOXGlJKXLdJpqFTcATVpgIgloCm6xONBSmV6nR0RqjXmzQaHplMFUPT2LuvC00TFPJ1QrbFyy+e4+prh3j5xXPcfucOApErsDSf9SJ8m0fTl7wz47PLMgkGBMGgwcJ8BaTK1pqWTrnscPWhITRNOQidP5djZEuSjt4IZM+Tf+m/07XlEHRdfsnf63iwMyHQUA1JkajFkft28djDJ5Yan+45spN6rUm12qSvL0YgYBCJBaAl6zQ/W2LQMnjn/Qx2yKK3L4ZuoOhSlraqktR+Fhut+SZgmORyNWXxKdr3RT2fni/RBRQKyla2Um6s24w2PMnTE5J7B6HuC16YksxUNnZa0zW4b1hg6YKtMcjU1LMWMeD6TpeHxnWKTcURH2rRRfYmBS9MS769fXMqi+VWKL7wo9b5qAqIk9hK5PofMj5RpTvegY1HMecsKX+ARGiCRMImuYkubCpskQpblOsup2bLVB2X49NlrhmJr9MU/22Kz2UpMDAwsOQOdejQIT788MMv5KT+KaPZbDI9PU1/f/+XYvy/xvB8yfHpMsWau1QFba6wW7uqU4HRp8Ylt/SpLEhfWP3TDl9K/sdJyXsLkut7vxhwqgmYz9TIFJsMDSZ4e36CRx86QSRiLVmU+r6kszvMjTdt4Wc/+RAroBMKmZw+2aDp+tx7ZCflYp1arXkREPTFhgfM+TqnQ2nuvrmTl6d9nIZkoyUn60jenJfcOSDQBDw/Kbm1X5XyVl0PlJh1tg7drc6ciAln8pKdCZWtvHhGTW0qOmxWdZNv2ByF4OouEEhenJHcPyLwWzzO+0dUZ/qq8wsY3Hzvbp557CSNukfFVYtJOGxww01bOP7R7IZndCmC3ysF0JWTkIfRcoNpb4wSSZtISPm3j5YkgUSIb3xnH6OfZMjnlPZtJBrg9KkFrrtheB3/bW1GCFgFpC+W5W1HzZN0bZD1aV/jfEMt2Df3KamiS8mCNjyVrf38wFSFJpSb2JMTcGTIpyesXRSY1l3JQ6OKUvBZgGnFlRuef8gQ3NIHr81Ibum/OBe2HU6jybFzGUKxFLcPmPSH2yBb4sggv/jHj7j9zq00mi7TU0V2X9ZFo+FRLTVoNnziiQAZ12dmpshtd26nXK5x9aEhyuU64eHrKW8wKJ+fUpqspqY2FW3uacVVnPyBiBrvpYKDaWn09EapVptUVvDNAVzPJ5erEQlbzM+V6e8xybXAUfGFHxH7+p8vZU89KcnVFfDeaOPZaavNzomcOh/D0ujuMLnv/l3kcg6FgqooJVJBIhGLZ58+TanU4LY7lSRWudTgtju28eO/eY8bbh7hxefPUSk3+IPv7Ec3BLZtEksGlp4VT0qCMZu6CwEDNLdJOplYagL1JRi6wHPVOHclxOJBZudLSuYuWyM5mCRsKM72azOSg12QttX9e2AEfnpWcnADVzrB8rxzRecK7q3b5NmTGW7dmqY/ZvLYmKQzqHjqW+OCuZqqMnVtpoXfypq2w+rcinvwf+Onj53Ck1m+/s8PU600cV1vSW1ESsCTVCoNUh1hxAYc93ZEggYHhuIcnyxSqjmcW6iyuzeyjrLw2xJfpgq/jF97ND2f9yaKZCtNmr4AoZOKWCTCq8sOfWHBnQOCl2ck87X1j6AmBPcMQulS2P2XGhLicRunWCMcMglYRkt30WF2pkSx6CA0QbXSpNn0uOveHVx73RD9AwkOXTvEvUd28f6702i6RqXc+NyuJ582Qgbc1Csx8Nkak+xJrv+brKMaaG7rV9Z8YyVlybdZcq7qql19O7psgeOp13UNqp5c5ZW9Nl6ekUxXoOjAQ+f9JamXzSLvSCYrELPgJ5/4/PKc5B8/URyutdNqbxjMRIivfHs/R47s4I4bB7jmtu185/tXcub04rL1o1j+x7J0unuiFwR7oLIx3S3BfliWQjM0BUyFqZPujnI65xPU1QJ5MuczJU0u29/NZfu6sUMmgaDOXffuWGrCWhlr7Sk3vB652gXtRt9ZkBScCzdBNT2WNneXWp63dAUOv8jYnxaYGjw5AXNV/6LA9Jfnle3pRuXWi4fkJ2cktU1+Qzoo2BqHF6ckmy/xy8c6kZM8NSHpDAoOdmsrgKmKUNjgznt28NyzZymXHIZGkkyM5TEM2LYzTaXa4MTxeWzbZOu2FJomiUZtGo1mqxt74994cx9c0aFUBT5alLw45fPanMomb4urZi/NEJiWvqTFZVka0WgANJVBFELgux5PPX6ajs4wpqUjcxNL4Kg+9h5kzwOqmvIPn0henJZLPMi1YWhKGaLN3TXcCoF3/xu9+jg9XQFSSQs7rNOoN3nysVNMT5e49vphnnvmEyrlBqapEQqbLC5WeOrxU1x7/RCVSoOf/uQDdF1nYb6yxAvPO5K//NgnlI6QjugENEGt3FQ0mRaXVdcEuq61qhsQtg2C4QCz2QY5B2TI5tVZNZe8NC25uluwa4VaSdQSRC2YrV54HKzdUI/EFe0poAtu7lVl/3Z028oGe6NYmTV1PAWo9at/yNNPj+LaPYR6hpmfK3HPkZ1YloHnySVXrmDQ4J7DO5kcL5Ape0xfoG/A0ASDaRuJxkK5ybmF6qZ/+5sev1tmrF/Gb1z4UnJ8qkyp7iE0nZF0iJ54AENfvy8SQhCxBPcMSR4852Ppku1x1dDTHVITRX9E4+WZz9sCpUIDRicr+K5Hw1EOUHcf3snTT5ymWlOztCYEtm1w0y1bQMAzT54hlbbxPMmZ003soMGNt6iM3aWCoI1CCLHk+93+/EavlRqqI7nt174yuzS0YUfyamB6rqhsOG/s27wRIVuX3LQiM11sSDJ1ld3U0Li1Dx4fk3x1y8bez0hI24L7RgRvz0veXoBDXRv/7qYveWJc8tUtSq5K1zWqruDlaZ/Hxz1A8s92r850DUfB9XXOO3Fu2a381oWUXHfDsGp+clyaLYH8cNjg3qO7CNr6BlSH9RG0de49uouHf3kcp+5jmqrsmAob3HPfLhqGzh0DknMFyfsZ6AwKzKbHmdEywmmQ7giRTIcIBHWVPV8zFjayp1wbF9vgTJYl39i2ItvqS+aqkkJDUHSU0PxCXfK1DRQPNgtfSl6Zllx2iV7klxo7EoL5mmSiDI+NwRUdki2x9c9+G5juXdFp/+lDsDclefC8GpsbUX+GohpFx+flKcnN/Rt/T95RfHdTV+YIiU0Gjgf09YX52jf3MTVVYHI8z/CWJIm4zeJimVg0wOBgHOlLqrUmhmHw8vPnmJou8s1v7UNsApB1ITB1ybszkq4Q7E6pLv7RouLjQ6uCETVxat6S5nIsHiQaD1KvNZiaLPDaq2Pcfud23nhjnFtu38bYQ/87lpRLm67iCz8i/LX/g6fHQ0RNtZGIr+M3t79PSVul2/K42fNUPngcPnic1PBBuq76IxYqQebLNgeu7CNoW7z68igL82XskMld9+zktVfHAKjXXaqVJrFYgHK5weJCGV0Xyvo0FeNXk5KICXOezm2Hd/PcEydxGz7HP5rl8NFdPPX4aQJBg0pZESLaNJqPj88St5SyxmUjUUy7vUPdOG7sERybUfanvmy7D27+90KIVdz7Dlvx+Nt0pa3xFgF2RVRdScgQ+JnzFM69i+dDw1ci+gtOnLquHKhMy2RmpsT5c1nuOaI0q0slh2g0gB0yOfbKGFu3pYkmg8xbYd6Y8/na1o1/XyJkLvm+mJ+CwvKbFl+C0y/j1xqTuTr5mosQOvsH40SD64dcW8turqVll0zZHO2NMtlQZcCJkiorz1aVT7Klqc7jyCYC5JtFpi55fU7Jg+ga7I/6PPnYSWIt/tRTj53i2LEx7jqsuFP5fJ3BoTiGofPS8+fQDY0/+uFBpibyLC5UGRyKY1k67707TaXc4PB9uz+1PNRGv727J4quC6aniqteO+/oVJpqcusJXfTQSmZkVjX8BFqT1HQF9iQ3LrG3P2Vq6jssTWUW3p6XqhmptdgLFMjaYH+h3heCqbKEbsFVXfAPZyRXdmwMZGcqqmQYNQSVssvkRIHFhQrDnWFuGIiT1QwmypLh1frhzNcUoGn7rUugo8vmK9+4jLNnVpfYXz82znU3DG+YyVwZGrA4X+P1Y+PcdMtWSqU6hYJDMmmzbXuaWMLCQ2VH3s8o3c8+t8axR05SbGV9gprKjt57ZGNpm5XZ2ZWl/XZcygbH0uFcEba2gMovz6oc3DU9kAqorHfE2nzzsVFk6oq6cCnj6tPGDb2Ct+YkUxXFLe8Ny6XNFXxRwFRRhz7OKtvJ1+cUPWht+d6TkumqKr++MedzaIWMUdOXnC9K3l+EsAkDEUGmArMFSU/n+u/TgFzOwQ4Z9PREaaaUTFKhUCXVEaLpeJw5uUCl1uTMyUU+ObNIT1+Ur371MsplxU3WW1apayOgww09glSrMbHLBteH8fLyffcB09bpCkVVdrHmMTtdZHq6RDhsce/R3bz15jgHrx4kxQzVqXfJN0EXSjjeO/8ucm6UodheLksqrvdmUXCUdFu5CZZbXsoCAjhjb+OMvU002kH33ruobP8+U9M1hkeSHLiyl3A4wKsvjzI/V14+XqFOKGxRLjdaWtJhZvJNqq1GpYgJWQdKTZsbHthPaVHJB4bjAf7Fv76GxfkyCwtVNE0QDlsc/2iWXLa2JPl2KfOw46lr+LOzPpWmeq56QipzrV8i7WO9asny505kJR9lJfujFVLP/GipbyKogx5OknfClMuLBMMGtVqDeDzFwnyFh3/5MbFYgHQ6xJkzC+Ra3fdXXNVHMVfj4L4op3IemwHpscXq0jtdsd9equKX4BS+bIT6J4hgQGesorHdlKsaEtZq2UlUA5Rm6Nx7325m9BBZx8fzIW7BVFkSteAfP5H8sz2sEEy+eMxUJY2WdlzIEmTny1TqHuVaDfP4LHce3UW15DA/W6a7N8rl+3oolOqcOl/ktju2EUsEmZguE06F2XNZN8Wi8t2+6ur+z6RbqgvB4nxV/XanBVSEWlBvuGlETbitrIhh6lx75y4mfJsjQ5dydMmrM9Bhw84V5aytMdXZnQ5u9AnVXT8cFVgtIDlegsvTgroHb87BNd0K4G6LXyDz6qhO3LYCg6mpbvQNdKIZjMJ8XWN6qsKP/+ZdavVm+2SwbZPv/dGVDPeHWSk8JlGqAzf1rf7+es3joZ8fx7R0bNtkfr5MuezgS3j4kRN873sH1nXwroyVjUpzcyWCQY1I1GZhvszxD2eXOoBfnZEkA7Av6vPgP5wkW/VIBQTNpk8uW0dKyc9/+iFHju7m2Sc/WaWzChCw1QK6sikKuOSF9dY+JR+VDihXtZv7BKOl1YoKnzY6baUz7EllavBFhkBwsAtmRiXdIXhqXHLPkMr+f1HAFFSDynd3CIKGGqsvTktu7mWJc+f6khda2eFcXVUQXpqGwYja+OYc6A/DoS7BcEyddyMMDy4K3pyVXNUrlzZ5AI2aR6nkUK81OXM6w+xMkXsO7yBoGwQtDc+VJNNhmnMlhkeSXH3NAH39MRrNJkE9yJuvT7BjZ+eGmyZdiJaM0XL4UjW6tcFpO6SU1D2JYQk6uyNYAYOZ2TKFfI3bbt9OPOyR/+n/W/nOB1SWvNJUjIDIsf8vt3z9P120cz8ZUHqkD4/6mJokcvU3iBz8xqq/EYEwzfh2yhm1WZyaKnD+XBa36ZHPr7ZIjseDnD6pMp+pdAhdh96ISW+vWCo29C+hEwPRmSRTT/L6rM/VGgxsTTKyLUm95jEzXfxM+tFDUTiZh2u61Mb5kVFJ1ZX85BOwdcmelGo0XLnBWemqdSYviW6yCZyuSM4UJF/dKkghqV3/DSpXf52mr9Q39GCUaCGBMKaREsrlBqGQRSBgUKs1KRYdpIRKRV2jYNAgHLKIRCwcV82nG8Wp2TLzhToCny0dNqHf14ao34UIBAKMjIz8zz6N39kIGkoI+rkJjy29cK4oCZuSoYhA16BT83n4kRNU6x6eVNkBQwPD93j28ZN8+7sHCHbpgHKQem5KlQh3J+DtOTjUvfl3N3xJuaE4qmcLSman7ilg5vqCxYzyRI9acG6mxujseaIRi56Eyak3SlTqLtXBPq64OkKsxcGs2kEWa1AWknR3hM6eyFLZ/VKBaTtbWqw2+fk/fojv+Uvd2b4nWczWeOzRkxy5bzfPPXMG15UYhssrT53k/m8fwNzU/WQ5ai7UPbmKZwUKrL42KznYuX5CfXdBcVj3ppbfuyytFvrb+lWH63RFbR4uFBEDDqSWwWhAV2LyG5VZBYLLgi4/+ot3qdWay8kAody2/v5v3+VP/+w6ApHlqerdBVUu7rZXLhrLjUaNhke57JB3VHbX0AR+w79go9HaRiWBwKlLnLribEkkp8dLzIXjzNWgPywoLhTB87A0cD1JqVhHoETBLyRt07ZT/YPv7mdutrRkKBFKR6npOqMliY+y8DyV9TmQlsQCy/fRNgRXdMLbC5I7BgS9YUFv+PNxRWULsFi/puYJTQgGIwpQCxRAvbVPcmyOLwSYQstdqzVMrumWvD0veHJCYghFham5qkGsP6xMEDJ11ezX8JVO6kaZQ8uyuP/ybnKO2hCnAxIX2B7XSJQq+J7PXEYpZbz84nluu2M7uinRXR/PkyQSAfr7o9TrLvW6R7Xi4HrgSweB3FT2aKPYk4RXZlQVY6OGLg/QAxpuNERFC7OvW+D7Pv78yVWNOJoQGJokqPnUPn6W+C1noXv/8nGkxPPXN0uCyqCWtAjalltXva4BxZxDYbFOveYihEZmoYrreiRToSWpO1g2qSgWHcIRSxmU5GukO8KbVgyklKQCcHUXvLcgsQyfW/sEekDpXg+L5Kefh4Xgyk54akJpah/oUI24MxUFXBdq8ItzSuHhQFo5hnV1LfOTOm3Ynlh9TB/JE2OSuCW4e1Bt8qedCM+Lm+mKqznx6i5VXomLMpZlorWu8wfvT3P4vl089vBJGg0XTVdNUcGgwT1HdnHyxBxHv7qXSlOZd6yNpuczV6ijodQRumK/3VJSX6YMv4xfa3hSAULb0vjmNo2vbBHsTQpqnhIZnp4uka94SFQZOW5BxBAEdYFs+izOtYWHVUaknR2qeap7tHkBcl61qcSUc3XlQnVdj+C7OwR3D2rcPSjY1muTDIilTJFE7VTn5iuYbgM9YjNT9okasiXtIekNwb602v1KKTfV6VsbQiihZ120HIgeOcn5c1mlkVl0yGaqNBuK++p5Pk6tST6rwHO12qBYdFiYK1OcLVxSySmgKyH7tWFqUPfZUKx9awwuW+PHXmsuC93f1KvkeD5YlBtmXttx+4DGUExZl36Y8ck5cKa48Tnrusb0ZEEB07UhlE/45EQBvcUhcH3lxrK28Wtto1G5qYB2UHrgNJFNl2y2xma9QRdqVPKkpNiAaqHGXYMa/2y3YGtCsLBYQ6BAjed6uJ5cRTFtS9usFdCG5ZLs4NYk+6/uY8KO82pWPRcR6VObyXP63WnSpQKlir9uora0tTSJzwfunp+S9P8aJM/aIZEs1OCVWWWL2ROC56dhR5wvBJiuD8HBLsHRYY17hjS+vlXj+zu1JacoDcFNvSr7NRThgiVt21BNXamAwDI17hjQ6AjpVEoOc3MVApZBR2cY2zZ58fmzSCnJLtbJZMroukbTVV3z42M5DMOguztEwDSYnChSqzU5P1nimUlJ7iIi7rYh6AoJHh+78N+9Ne8jPQ/f91c14qy+OiBdh0Zxkdyz/x9m82XeWZA8N+nz4zOKgrFR3NYvNqxWOTWPhfkKzz/zCS+9cA7bNjhy3y4MQ6daaZBKhRBCLAGt14+NE45Y/MF39uN5Hp1dYQKXANA7bcHdQxpTZZWZBD7VPLw2um21JphCmZQI4MZe5VioNIQFvoSXp9d/NhFYnTXNOpKnxyVbooIbexWH/oOM5PlppQtdbbmXQVsa0efuwztbTV6CQsHh9VfHuf9re/jGt/axZ283d969g7vu3cm770xy0y1bsQIaY+X1OtWeL/lgorh0NlcNx37rnaJ+7zOnjUaDubk5uru7L+gJ+2V8tnCaHo6nvM9VqKxFb1iV2977pEb8AgtDPldjREshpQKzl6UkU2U1ee5IKLB0z9DGGZ9EQIn6r4ysIzmdl5wt+FybiFBHo9IqqScsSbHugaFjBQxiHRHc3MXFvy8UazmlwyNJHnv4BJGoRTZTQ/qqQcH1lMtLIGCgaQLPkxQKdQIBg1Kx3QIqmRjPM7IlibgI31YTgv6Iam5KrRDUFwiu7VJWfPcOrT7GerF3ZfM5V5Us1tR/d4cUF6+23uBrXWQdyVvzChCfLyoR9Cs6Vn+HoQkWF1ashCvXlxbfP7NYwTJ6qHk+L05LLA2OzUr2JiUdLWmYlY1Gjg8WknK+vnrBkpBZqJNIB9ZlV9Y2KkkknuuhGzp1T73f22kjUdd2vCjZ1rH8976ntGM9X91zIVTpcn5e8ew2kxiTUpKrS1JBuLFHW0VxATiLpI7OPfftZttAaOm8I6aqBtzS92nG5sZjueJKFutsaBn7RYRE8vKMUn0oNFRp/eZewetzktESbImtHqO/zig2JMezqulJApel1aa3HWubEF3XpVIuo4kw25sVcvM1Zms2e3amqFoGQjTo6Arz2qtj/MF39/PTH3/A66+Mc9udW6lWTc6fy+E2PaKJIEPDSYK2Ri7vYJgaxaKD47jUCzXu2plc1zy3UVyegg8W1SZtw2ZElBJHZ5vTu0a+aOlvdJ9mYRFdQP2jJxm4489wwvsIm0pO6o051ZTUuUbOa2CDpkshVDNTvd6kXFFyes89e5bb7tjK9//4SjKLVer1Jv9/9v7zSY4rTfNEf8dFeGiZkVohITIBkgBBrbWAIFlVLK26p3emb+/c/Tt27O6HuWZrtnbvnZ0Z657p7hIsRa21AAEqECC0TK1Daw93P/fDiVRAAgRZ1TVTTbxmNJLIyECEi+PPed9HpFJBIlE/05N57rlv6LI+p19W39sk+O0ZyVCEP9oq6dN5pfi/t1vw8rgKCllJ6hIMxyWHM8rXPZ/PE4/HV2XES07n4UReKfBvaV+xQHM8JZz81pACsSETMg3oNdS1lUgE+fSjKe57aBN4klyuRjweQEqJz1Lc80ymgqYLnvj2taQSPhqoqeE/n5Lc1qE+H0Cx1qTacBB4jHSG1tV2/KXVX/43+CNLSkmz2fxau66rdflquh6jmToxH2xL6XhSjaSQEl0TeFKso1yW5G3Fe/Ppgkg0wPnzeRbmK0TjAcJtEVyp4XmS8aIayT1zTrJ7YCVRx/GU8nL16GvJI7U9ADe1C25OC0zNIPTECL97+gR2U4EBKcHVdfbsHcGMG9xufb3rQtM0DA3mZiq8/LwCG6GwD9eVTE8V6e6J0tsXx/Ukmqb+Xsf11tj+LPGyvFZXQNM1olE/87MlOvtjX3rNxnxSWXddUEFDAbgrKV0TxC1J1AcVBwyhQgVOF778dw8vqqzuA7Oqa3YipwDqSma4Oldt6dBy0suaav1/si3I2LkcsZifkYTBgTmPuqPU31J6mLrixXYnI9hCA+lSLq4FppbfIBTy8cKzxy4YoyrAtp5QacluyJHQHjXo6FoRKg1FQWuG1eubLsaSWKwFbIIBFQhQqdggLq/Aj1vKi/RCc35VAj8eb758At+T22kYOkNRwXOjX+26PF1Qo9qRxMU/myzD9jbxlczpr7SWgGlPUNFn8g24Pq3s4u7uFrw/LXlpTLJnUPEa/yXrRE5tTG/vVLue1cBr7SayTjIVoL0zguXXWKzqPPv8YXzSw0NNYrzGIO3tKvnpg/dGueW2fj7/dKKVLlfm6BdzbBhKsWEogW5olEuN1nmVGIZgdrpItWKrez5kUs7WiSWsL3WUUClqyoHjUl7P5ZYV3KW6pgDCsZFNFbWKXaXy1v+Hzd//j8vc060JyfutVLd0QLA9tf7mVX0mqFaalIsNnJZ39fxcmad+eZho1CLdEUYArufhs3QGhhJ4LsTbLJqO/Eqj+KXyaYKkpSztUn9EUMNEWXK6oEB/V0iwe0BtpFfHyEZ9qsvuOBLXdbhwoao4yrR/9fMmRI2jFT+94ZXu6khcpbh1h1Tn3gro3HbHAE/94TjXDCe5+aZeQOK4kpmpIo16kw1DSdraQkRCOkstCp8uuCYJn8xLbmpfWndW/u6G81WP5v+c9Y0Hp1frX64Ut0nQ9ARx02Xf6ezyw70j5mdLZ3gd5bIg5pO4UmDqgqbj8sarp5eXg6Clc9ejI4hQgLmKgg/lJjx9TmLpEr+hzMTLTfj2BgiYcL4IhxdVjOIdqxZ0D/ioHmDP97YjimXyuSpawGTLYJxQyMSDr+wIoAPVisPURIFwxOLF544jJWiaytTOLlbwPAVQb7mtH79lLH931X3TQAj8fn2Zl7Xkd2caGqGQj5mZMr0DMZqXwSe6rrEpaZKyBA4Otr2sF+dYTjK0js/kejVb8fCkWhCrjsRoGYPn5pW9VOoyHa/7WlY9ewZVTOnni8qO5sCcGn9fmxTc3OHS2xcj4DdXxFDqYwIQCBh0dUX5v/7PDwj4lUDq+0MhbCTvTCoRS8QHx7KSXEhj92MjvPHcUUoXANNdu4c5emR2jcn9+YJHxRFsb1Oju9VCpYbtIIRAehD3wX33bySXrS6r79sC8GZ25fXSk62Hk8S0DHbtGubzg1NUK01CIZN0exgNLgk+1jPnX12G61JcLHNEj3Eko85lX/iKTiGgqBh7BtY/V4WG4iL/qWs1MB2KCWqOx3QV7rFUWtUrY2rcuX9W8vqE5JE+LmlltPo9v4oLwVK5UonovrdRELW0Nd3R1aLMel3ZkHmeJBT28Z3vXcuLTx9HuB6YOpoQBE3o7YtjGBqNho/hkTSTEznuuncjMzNFAHr7E2gGZLM1AgGDRsPF59PRNPj4w0kSyRDFYoO2dIhI2OLlF0+y+7ERIjHflwK17qCgP3rpnwcN1T31ad76wiXp4eYmcBqKT60JgWYFsYSznFYVMBRnEmC+Jik1IX4J4beQYFk6gZCJcYHqsVhsUGxNfgYGk9RqDtVyjVDIoukE/qimUNIvKNiQ+prXbs2RvDUp+f4mscyHbw/AaxOwo80juJ6C86ISXN+29k98ToXyp08hN36fkfgKVyZmCQYiisN/a8cK9/zB72zHLJeZnMjT2RXBtl26uiMEwhZ1T2D55DIwXartKXhuVHFwF0sNzs6vTJ++zFf6L6WugtOr9S9WuibojPmYzDXU40SuDBXnCnX6kgFC6yiXBeD3adx2+wBHv5hBFyyrvRu2yysvnOCR72yn2tQotFbTpqf+QSqj5LMFeHZUEregzS94cqO4iKv5/KhHtg6Tlsa9mxK4boRjkxnenPPYO/R1vrFgdrrMb355CMPUuPGmXuZmlYdfIhmkkK/R1aWeKp4n+ezTSXbtGeaVl04tcy5dz6O9PcStt/Wz74Ox5Xe2LMXVOvjpJFuv6SRXdjlbVeb4KT9sjCkxWdkRBOwVS6ZUOkhvbwx/wMAyBFWp0o0e7F2lOvYk02VIB9WDzfWUVdJMRXEtn9igXvfGpOSRPtUJeKAXXhiVPDaoEfQJXE9FAV+qOoLw/Y0CTVMbB9eDKZW6SChs8OOf7eSXS2r9VcD0Rz/dycf7xwGo1dcKpO7vVZZShQY80APnSpD3gux9YhtTEwUKhTrRmJ9wxMexI3PLnNJ8rkbETuBzXYxCmc/Pr9h1ta0SKi3MV9B1QShk8clHE2usalLtAZAe0bR6/fxsadnaJhAw+XDfGPPzZSzL4PY7B3nx+ePccecg6fbQ8ghz9Qh5Pc6rI5Uq19RUx9ot1/jRTQlydbVhuFSO97mi4uUuNaMF6ry+PiHx6XINx1oARRt61vXI/fp1ITAFCBgaft3jmTOSb20U3NcreH1C8mif4MM5lQ73QN+lNzyFhuRYDm7v/Oqfp+kpnrDlukycK6yxaAN4+YUTVCo2uWwNXRctm6MGp04s0Gx6lEsNhBBEYxYRv4HjuDQaTfwBE5+lk24PUyzU6OmJUq+7uJ6HafqIx/zUG6pjWq05vPjscW6+tY8P3hslEDTZ89gI2WyVbLbK2dMZrrmuA926PCjameai5LTV9WCv+v0yEbhAuATgWzhG7oX/g5KtnE+WVmWj/0ZIxy56/Xoxx0ulAYV8g0q5iaHrhEImxUId74Ixgd9vEI74SMQD5DIO7ek/DpiCWu8bX9ZqvkydzsOWhFgj1NSE4IEeRWPqj1z6dy9b2fNMvvH3bOi/GXxr8/raAqrjuXTMm1JypKyxeyBO0BTL0xW1Lkg+m/awNLipY639n0+H3a3N5tn5Ck3HQQA9cYvBr4vW/yerq+D0av2L1qb2EFG/QanuEg8ahCydA+cKSKDedAn49IuUy5F4gFDEzyuvnWExo0QsEvBavnRO06WeK2P5Y3ynV3ka7mgTCKl2+Jom2BJTO1Xbk5dUINcc1WEo25Cru4R1j7TlMefBVGXtaOdytdQtdRyPX//z59QbDulwiHx+BWwUW/zRYMjEsgwaDYf5uTIfHZhg195hioUGpVKdvv44bekQ+VyN67Z3USioEaPPZ7B/3xhO0yMaDzA1WSTZHcenebw7DZ/Nw8aERl+jwn//p4NrBEZ+vwJ/0biFHjLZGF/7+d+aVMkwpdbY3tJhR5tgICw4lpOEW2Z+d3QK9s0qrmpAEzzU5jJxKkdmsUoqHaSnN0YwZKzbHVw9sl1Kn1rqxDhAV0+Iv/vfblOgerFCKhWkqyvKxwfGOXBgYuWc1ZRAasu1aVzXoz2wEhe4OaaA7+hckwNfLBAKmdQnS4iGvbwxcZHo4QCVTI23XzpG014B1KuBZ0dXhMOfT1GpOlTLK8fStt1lhXVHUON0XjKS0JetbaoVh+NH5xkYTHDdji4sy+DDD0ZZWKgwNVlk72NbiScCxOLWGh/bru4oyQsoLksPqlJT0T6icUUNiF6Gow0wFBWX7Iy/Mu7xQO/aEeQ7Ux6ZGn8yQdR6wFRF2qoAhakq/PqM5IebBQ/0Ct6ekTzQrbLJ981I7u6+eIRcaEg+nFOxpl+lNKBRdylma2y0PX75308i5coYNJEIsH1nF7W6Qy5bw/MkkahFsVCnozNCLlfDdTw0TWDbLoV8neTGFKOjOTZvbsNxmvj9Csim2sIgJJYl0XQDz4VCsYHP1Mnn6pRKNnse38q5sxl2XN/Fho0pPnx/lA1DSYqFBtlsjdnFKrVwmLmqotJct45Y7Mu6y5crn1Oh+PZ/QjaqNBqgmu8t3uLb/2lNpOmVVLPhcfZ0hs8/m+K+Bzdy3wObePP108zNlpcBqt9vsGvvMOl0GMd1Saf96IGLed9ftbqCSwlPX+94mDqsF0TWE/76OnGfU6HwjvI0rb+/NiK24kjemJBrQO/RrFqzPp6X3NW18udL3PS7uuBwRvIPx2EwoiYNoO7f8NI6GvIxX/AQeCRC5poR/19yfePBqWmadHZ2YprreDNcrT9JtUct2leNoXRN4HlQbrgkQmuVywMigRCCgx9N4ZTqxFc9iBuepOGqLlApX6NnU4K4JXm0H5bUMxc6S13aGkcBMg+VXvSHc/BXwzqJeII7UyZvTqnd87bk+lnTy98FmJ2u8O5b57hueyeNhoPreFQrNrGYAhtSguN4BEOCAx+O8+ieYV558SS6rrEwX+a5p4/RNxDjzrs2cPDgFAvvVLjtjgEOfz5NPBng7NlFcpkafr/B9364nS++mCHREWM6HGemDClL8cy2+R3+7/968CLle73u8Kt/Psi//btboewwWdKYj690RAxNgYgdF4iVKo7kbEHFo2pCjdDkqu/81C8OUavZrYc9BAI+fviTHXR0h5YBaqkpmSxDw1G/Oxhdn1voAFbY4Lod7Zw/W+CD98/zu6e+UD+84OWZxQqG1o67DgoOmxBpC+PHpZy10cWKql0iaaAz0BXmmd9+sQaYwgrw/MFPdjA3U2JuroIQF/sYLlEDZDCO3xAtmxyJgxrNnzq5gGUZnDgxTyFfV1emEDTqDsVCnYOfTbHzhh7effvc8nv6fDpPPHnNGoqLqYH0WhxWv44dDvPSmMutHeJr8TMbrlwWdK0tgSNhvLT04Pz6D7f1gamy/9IE7Exr7ETy3HnJH86qdJ67OgVvTEke7hO8Oy15Y1Jyfw/LIqnVwHQ9O7JLlQ4szFV57umj3H7nIK+8eBLbVrnl0Zgfw9TQTY3pySJOa5Sv6yuZ5kv3sOt66joQ6j6uVW3suuIZl0oSw/RIJIK4rodp6ITDPuUuISGTqXDyxALJVIi2dIjxsRyhkIXfb/DGq6e5dnsn+1uTgVjcT3axSjQWYWtifbugP7paAqlW6ieFBmhC/V31sc+JZs9D+7Vf+jagrulctkouW2VxocIXh2a49bZ+vvPda8nlaizMVwhHfLR3RAgGDTxPYlcqBM8+gxH/PrYRYr4mSQcuFwpy6WoPwifz8KvTHhuigvaAoros3e9TFaUxuNz6PVGSV+wWoesGiXgCXb8MbMqep3r+ILD+8ewLQ9cqRHwqL3l8UPD7c5Kd6fUiiZXN2jVJyS9OSX53VvL4hrXPtZC10kaPBf71QLpvvJWUpmkEg8GrRvx/xjI0gUCSvyDIeckSxPXkRUKpJWAaMRWgjCcCnMp5NNyvt3M+lYeoBSMJQdxSu9DjeYHPsvAbGrsHBD4dJsqXf59qxeE3vzxEKGySyVSXR1XFYkN1SVuqSc+TaJpGZrHCgQ/H2LV3mIcf3cINN/Vy7/0buenmPqJRi+HhdgY3KC/Ob333WjZvaWPjxjYefHgzj+wexnU9Tp1YpCMVoNpsqbUF3NalMzVxCUsmoFZzmJkuUizW2d5urLrxJeOl9UdYubqyLFoyfPbpqktZrTg89YvPKZcbuK7E8ySuKymXG/z6l59TrSgpf6aucq19GvSEldr/6XOS0eKlx3mN1vucPrW4cloveHmqLbSuFdbSiz8v6ex5fIRowFjzYHJ1nccf30q5VMO2HWruxRnrtu22Hrg1NKFd8qG5sFijs1GmeGqaiXM5mjUXozWaLxbqTE7kVSfOVcfGdT2QkC8oH8hioU4o7Fvz97771jke3TOMrzWzdaUaW3ZGdH747a10Jw3aA4L3phWH9FhW4n1pRvxKTZWhc52J3w1pZalUav5xzhSXBqZKoLi0+dEQPD6oKClPn5Uk/Kor/+akAqCmBu9PK7HL1wOmikdaLNj86p8+p2G7lIoNarUmruvhecoJw3U88tkayVSQZktEomkCt/XfS/dwIGjirqKsFPLq3H304QTt7QEEGrVaA59lYpoajbrD3GyZbLaGz9LZsDFFPO5HCEEyGaRpOwgB11zXyUf7x8ksVPAHDKIRi0TUx2BEAXPrTyxQWy2Q0hR2JmapzX6+oaI2c2/9Z3zOJXykLihNQGaxSjSuLqqJ8QKj5/P803/7jI8OTNBsujhNj9FzWV587gS/feoLCqUGsmMEgcfzox6n8pL6FTh/rFdhU/FidaGmMIt15Uv69DmPX572+CJz4d29trqC8FDflR9jIdTz4VKdyaXja3sryVHFd1aOZ8hQG/z08j0oKdmqE35fjxIH2pdY13Qh+NmwYENU8MKomjA0Wu3VsLUCSP+1iKHgaucUx3EoFotEo9FV9hBX61+0BEgEvksQzi9UTjc91eWMW2qHHQloGPEwXa6KcbzQ8/LLKt+QHM1KkIqYfiwHW5OCmOFRLldbmxX9S99X07RlQLiwUGHjprY1bjAHPhzn0d3DvPay4pR6rkc06qdUVt3Gg59N0bRdfJZOJNLFb5/6gnDEx+13DPD6q2do1JuIlpBq7LzH7XcN8tH+CXw+nWjcz942wVRF4ngwFNN4d+HyD5XMYpVI1E93WCdhqO7cUvPw0CLc17P29WET/MZK9Omn85J7egymxhYol23Wq3LJZmqiAD1tFOqSlF+wsQVUEi3ng/yF7H4Uv+p0AdKBSwikWhUImPT2xRTYW6fGSi37rMHgGqpINB7ADoeZEQb+8RlF/7jEcymbqZJIBXA9F027GKA2HQ8pJR++cxa7tcHy+XT2PrGNeDxAuWyrB9jStdD6T9f1iEUtTlYayx6olVXHcX6ujOO6fO/H25mbWTHnX516c0NasCkGH8xKZitwYA7+ZuuXP2BdT3KmINeNnI34BEm/XPYA/Tp1pcB0qTQheKRP8uIYvDwm2T0guLlD8Mak6qC+MSl5YUwSMlSc5ZUC02NZ5at7a1Jy9nSGWr1JZzxCIV9bvjdd10OTgkbDVRONgEkwaFIuNXA9iX/VQTr46RR7H9/Kb399mNoSgNUFhz+f5r4HNrF/3wR33D2A5xlI6eF5GpbfQNM1kALdgH0fnGXsXBbXlQRDPh54eBPHvpjlwH5FV1kS7J06ucBDj27+WlzM0aISK65n9bRca2ylBD5NJeb5dHX+bRcWzxxEzJ7nfbkNXcBdXZcGyZ6EUNiHYeiYPoNySYH5RsPl7OkMZ09nMAyNcNhHPl+nozNMujPFGJ1MzHnUHBWS8cGM5L6e9eONv6xmqoqKVLYlN7dr3NwukEieG1Xf53Lo9FLuA5cqz3OpVqurmlkX/H72PLXRg9QcltO9VndPHU+JSJfoTGcLK3HBG6IwVxP86rTkuxvX66CqZ98NabUGjJUkz49Kvr0B4kGTsN+gUm9ybLrMTYOxdUMa/tLqG4/GXNcln88TCoWugtM/U/lNjXrTpdFcO5c9vChbCVJKPHHHI8O88sIJKk0XTaibc4kX+G5FZ3NMjYu/ah3LsvwA1IRgvOSxq1/guR6ZTAW/3492iRSm1SIWXVMjZikl05NF0u3KiHspcm5+rsz+fapLWi6pGE3LpzO0McX4eI5UKkQs5qe9I4Trely3vWv5d7/15DVkM1XGx3JEo378AZOPD4yDULntmYUyTjjAoUWPh/vFiiXTZSrVppJayrbL+Ry0B5VpNKjO5oWVa6yIU96Y9Cg3oT8seHf+8iB4caHC1s0dTJbcNYBnCU52tBbkLzKqaxb3wTtTkqApOFUQPDlorgikVnWCAwGTH/90J6GwwaVOe1dIAaHnRz2e2KCoIjE7wQczHos5yS2dIKKKuxm5hBNDMOSjvT2MYSj6yepnkPSUsCUSsZaBKajO59tvnOHeBzdiXmLT5fcbBIM+SsXGGg/U1TU/W6arN3rZ1JuoT7C7X1BzJL86LZmryeW0rExdEvOtfdC7njIC7wyq8Ir1KlNXue3XJr969/SrAtOlagtoXJfyOJKF1yYUKL0hLXhrSnVK35qS3JC+cmCabSjfVE0IRmcrZLJVkCoaMhpb5WErl/6R1KoOH+4b44lvb+Of/ttB6vUmmq5G+JZlcMNNvXx0YJzdj420eKN1BoeS2A2XY0dmufGWPg4dnCUUNtm6LY3Pp54h1YrN3FyFaMxPV2cUn6GTbAvS0RHB79dJJIPcfEsfsZifcMTi1MkFbrtj4IqjNy/0Yz1TkMsG7+vVerZSaz131aTIp4P24X/mse/8B87Ug+van9meVJ1BCR2dEV5/5TS79w7z8gsn19CWbNshFvO3NmIGex/fyucHp7jplj6Gev54g3h1vlVK2rOjkhvbl76J4InBPz048zyPSqWCblrsWxBrRKVLx9eVS13TlZ8V3/nPRL7zv/PWbIjbWjZmp/Iep/Pw8LLXtOC2DjX2/90ZyV+NXP7zD0TU+X9+THJPN2zuDHNovEDVdpnON+hNXCYl5S+krqKxq/Vnr4jfIF91qNruGjPp7W2C69qUFZShgSmC/PynOzg3WaJZrtHWUtfOezq1godPUxFzX/VhekeLeK7UjpLBiCKYX+6h4DM06lWX2ZkCmcUqiVSQ3t4oqXRouSPzzptn+eFPrucX//gZ9dasan6uzGuvnObHP72eg59NMjtT5pOPJ7nnviEWF8rMTBc4+FmzlSCl8cjuLYDkv/3XT+jujXLn3YPk8zXmZ0vcfe8Qsbif/R+MMnxNJ+eKkodaEXmu66mOY8Bcd7QfCBh0dUcxDA1puXQHVLc06lMcqM2xtcew6kgOLkhu7VR/PhhR47NGwyHVFrzs8U2mgsQMj9EiJP0qFjNkqs+5ZwBeHJM8sQG+yEp6ggpM3NMmmZ0pksnWOO8F6B+IrxFItbWF6OmNUbcMjuaVLdi650lT3YXxksR2laH2uYIaqbcH4Miix93tYdrC+kWcU1Ad0I7OCLol2f3YVl598TTVqo3jSExTw2dqPLp7C8ePzF70u7qhMTNV4OFdW3j/nfOUy/YaUciju4c5fGgay9/yQF2n+7zkhyqlXAYPnlShExd2NgOGYM8gvDgqAaW8lignhG9tUD93pQKmWxOChCX5/Vlo88vlbvZSDUYFlfUZIZetrwtMl+qapCDbkMxU4M1JBTQ8BG9PK7D61pRksSbpi4jL2pZlG8r788FeQdDUOHaoSqI1bs7namuEiEul6xq2dCjk60gk/8vf3szpU2oq0N0Tpdn02L9vDNfxeOHccSIRi4cf3YzQBE/94hCgBHr3P7yJzGKVg5/OEIv7aW8PI6U6567jEU8oACqQGKZGMGQysq2dXLZKNlPF8us89OjmKwKmF4Z6LDkO9EV0cg1J1yX2p+vZSolWQMdFR1Wo1/dfogs7VVYbS01IhqIat97RvwxKy+UGpWKdn/71Tiplm6nJAoGAyeCGJM2mEp1dLkb4q1TSEsv2aI8PXvp1mqYtB2R4nrec1gd85WQpz1Od3p4L7p+lrrShKdeU1VUf+5xIdpSu0DUMJxSF5mgWHh8UF3WLe0Lqnj1flGz4Eru/vrAg7lPeqbsGdOJBk1zZY7ZwFZxerav1tao7ZjGRrdN0PM7MlRnpWiE8CgRL/G4P0AMaA0NxYlZiOfLt1eMeG2Pw3sxKF+6rlbrpfa0G17bkpV9pCUGl6jC7UOK5Z4+BlBiGjuO4aLrGT39+w3K39MTxBXp6o/w//v1tTEzklYo9FSSVDnH82Cy33j7A/HyZsfM5bNvhkd3DjI3mKOTrDG1Mkm4Ps7hYJp+r8df/9iZKxTozU0UCAWVHtH/fKD19cWZmytx+d5AtHcrCaanWWDKt6TgqSybdEOghA0MowGO0FuyKI1u2QiuL4bkibIiKZceCjTEYLQump4t09UTx+41lAL66AgGDrp4oc/NVrktZfDwn+WxBcnO7skW6JiG4q0vw7HnJtUnBrR0qzvX535ygUncoNVsxka0O+YataSq5NJuSynB//6TkpgtVb+tUrrHSqZ2pKmVu2i9IBSTHCjr37RrhnZdPrvEVXfo7rYCO4zkYhsYddw1QLKgxfKotSDIZYt++UcrFi7kJtVoTXdc4dmyOBx7ZjG07TIzliUT9BIMmB/aPo+mC3XtGOLoOuF0Cxhc+LBdqKrEnaFxss6TiFyHbgKYLb01JntggeGVc+f5GfapD3B1SHZvHBtW43PbUpmypK7mzDUo2fJWN3uVU+VcCTEGNlB/ohfMFpVh+cUz5seoocdSDvYLRkuSVcSUECepQsNX7l5uSmqPGuGcKyh7N0pUdTyhioesaPp+O43gc+HCcXa3uXqPhoGkCXRdousbeJ7by3NPHSSYD3PfAJkqlOrWqTWdXhPsf3MjMdIlAwMAfMHE9yVuvnQEgEDS58eY+Th6bY3CoTcWUZqo0bQ/Tp2FZBiePzWJYOgJBNlvlvgc30pQS3dJId0fo6IlccSa8ij6u8tILJ2ja7vLE2ufTeWDXMAu+Sy+GZW3FVsqTkremJFkPvj0o1hUMXY5qH1kl1Dqek5zWAjzw0GYWpwo0bZee3hi6plHI10m1hdF1wUsvnOCBhzYhuXRa2h9TF47ohVDfq1RUADmzUCHVFqJ/IE7DdpmeLFAqNki3h+nsjlzRxuBsQXJsTpJuWxskcmFXer2Jeumd/8z27/wHbIKcyEse6r0YmC7Vg72Cj+clG6LwZfdj2KfAsONCTyJAtmxTbrjUmy7+q/GlV+tqXXnVbJeZQsv3FI+FYoNkyEd7dH3TRp8m8Glqhwssx2Y2PZXqMfklgqWvWxpK7Ts5WSaTqfDyCydxXY9Q2Ec+V0FoglDIZHa2wA9/soNf/tPn1GpN3njtLFOTRR58ZDNt6RBzsyUyixW2XtPFe++co9n0uH5nNxIYPZ8lmQzS0aFGyONjORq2w9Zt7SzMV5iYKJBuC9HRGeGtN06zaXOa40dm2f3YCK7rLo/cKo4CLg7iYkumtiA9PTECAQPTFPyXEx6PbxBIqTikT2xQC+F8dS3Qbw/Akt2iRHIyp3hdNdej06/zgx/v4KlfHloDUAMBg+//aAcBv8Gh43PsuKkb2/NYqKmEqLMF1dHcOyjYHFdvvjoVydRU+k3Tk9BSzu/53nZOZARfLCiLK8eF2QpsisPlFu6QAc+dk5iaShzzG0qIMFGCjRGJrq/lpF7I7WzW4bmnj+J5gnDYIhAwyZfqNGyP8fM5EsnARcKIStmmozPCJx9P8tbrZ+jujXLNtZ3k8jXm5irs2NnN1m3t2LbLR/vXdk1XA+MLH5KWDnUXPl+Ua0aJy8fdEPS0VvInLSWw0JDEfeqcblhlXRmzBNuSKi2pbMPNLaD/7rTqcH976MrAqYfk/WmVFraeKv9KgOlSCQRDMUWT+XRe8ukC3JhWv//2lEqS6gjA+zNK6BQyVRffEDBelvSH1Zj+5XFJzAS/4bElGeazj0/wyO5hXnr+BPPzimLzyO4t1GtqUhGOWNi2i22r7un8XJnTpxbp6o4wMJgkEPRRrdokU0ECAYN0R5jsYpVt13Zw930b6OmOUW806e5NMDVZIBy26OmNYzcdKmWbV186hdP0qFZt4skAjzw6TD5bJd4KcvBWdcfXq2xDjc9jpsCxPRq2x/PPHKPW+vyeq8SjdsPh9RdP8PCTO1Cr1uXrvWlJ2ID7N4or5iYudRs1DbpNwS7LQ8PD0NSa/MmiwUZLp7MrwqsvnaJetwmGfFQrNrbt8cjuYYqFOrVq87JpaRfSFb5qLXWWVSKXx29+eUh1ywWk0yFuvX2AA/vGVHSs7aAJQVs6xGPf2kawLcBUWW2mB8KKix23FC90/xxU68rZJX3htX1BROx6LgtL3NNnK9uI+gSRy9jB9UcU5WzJ6vBy9dGcuv98usAXNPH7dBq2x1zRZuAv3O/0Gw9ONU0jHA5fVev/GarWdPlkrIASLq8cb+MrqFJDhuDWDmVPdCwHO1JfPzXmwhJCw+8PEDB95LJ1DEPnuaePsf36LhoNh1jcT7FQX1b0BoM+zp7OEk/4+dt/fysT46pbmkwFaTRczpxaYHiknVq9SWaxQihk0d0TpVSyOXpklj2PjfD6K6fd9MZMAAEAAElEQVQZHkkjga6eCEjB6LkcdtNlw1CSQMCkXG5w08192E2Xu+4dolptouk6r014lBqS7rDg1hbIWLJk2nJtmut8nQgpaHoqIWq+7hE11QL+2oRcXviSlnrwf3cjLAG+1cbbuYbKsgcw2sOcPZOhqyfaimqskM2o75xuD+GzNE6enCfdFiRqCa5NwWvjcE+3xmLNY0t8xZppvVSkJVsqUDzO0mKJG9MxhFDq2qfPST5ZgCNZybeHVj7vhRX2QUiHiYoS1HSGljrDq1+vr8vtFEKwMFvGaUo0XVAp21TKNqWmZOvWDkyfTrPp4rOMNYILn08nHPGxa48KlZieLDI9WSQU9hGO+kj3d5PBoCdhXBYYX1hxS7Appmy9Pl/wSAXEJcVLSx6Yd3UL3puRbE+vtcLK1iVjJcmuVSKjyYpkoQ4/2fzl95DjSY5kYaYqGYmLNaPHKx3lr1fzNRUt+vgGwUtjkqgJm+MKqLw3rcb9u/svXKMFvRHJkYzy3hUCqg40XYEUOrfdPsCHH5znkd1bVLxmuUHTdtm4uY1DB6eIRC1ee/kUsbh/mSfpuh4z0yWmp0qcObXA7se28sXhGTo6Ivz6F4e474Eh7rpnANtWr1uYL5NIBNm4qQ3HcfAkVAsO8USQu+8bUsENpoblN6k3mrzx3BlCIZNde0do6wjiXgKEHc1K5qpwd49GMVen2XTJ5+rMz1VUclxhReCl4lFtSnMFuhIp3C8BdeeK8JMtF8dtVrgYzOhC0Ki5zE4XWVysEg77iMb8eK5HMhVEDxkczkiihiRkWjz3h2Pcec9ga9pQIxbzEwiafPbJJLfc1k+z6a47HbgUXeFKObhL75GZr/H8M8fY+8RWfvVPB5c3z4ahcdMtfbz0vIqqTqWCNGwHx5PMzVd47rnjPPTkdqqeztaEoOmq9LnxkuSNmtrc7khCSAQw9JUNwMVcXrGs1L+wsm/9Zwbu/t/Z0f3l0W5Jv9qQXg6cHsmq++3aVVxjn67RAOx/Bar9bzw4NU2T9vb2/9Ef4xtRxZqjeD9oDLYFCVkGEb9xSdX+epVviR7u7RYcy0oOZ+D69B//2YQQhCwTXUYpFRVXcHIyj24ICoUamqY6FUtiCljxQdz33iiDgwn6B+IEAyaz82VKxQapthDvvnWG4a0dHDo0Q63aZMf13fzDf/2YZtPlrTfOcOvt/fzmV4ex/DrdPTFuvLGH0fNZPM9jfraEQFCuNNh+fTe6LqhWbIQuyC6WGez2c9SWbEuoEbbnKXBlCMD1qNZWG8xr9Pp93OI5vDTuUHeUEvejeckDPYK5quREbv3s9YWasty6IQ2WbpBzI8xMFOgdSBKJ+rD8Bj6fRjBoMjNd4KN9E+x6bCunjy7Q0xfj2jYDUGrwQ4uSLXFQRtIXpyJJWse3td7mczV2bEwsd86FkGyOC4Zb77FeFRpKFXv/RsG505LyZbiUcp3ula4JcrkauqGvBZ86HPhshl17htd4lKrjqzqfhk8j1R7gyR9uZ3K6RKmgnALSHREiYZ3fnPG4q1vQFVwfGK+uz+Y9+iKQDmjc2qERMiVfLEpuugL/y6Rf5YG/OqZEYD0hJZrbN6tSvlaLjD6aVWPGLxshzlUlny5IOoOCe7sFQeNPB0wPLUru61EPdr+uaCQAHUF13b01Kbm/92LPyiXPyLemJPf3CCVyM1UXNufXeejRYU4en6dhO2ze0obQIJet8tkn05w9k10Gpfv3jbH3ia2Uyw2KhQY9fVGQUK3aHDk8R/fuGH6/wZuvn+XIF3Pcfe8GYnE/AwMJGo0mzaaDrgn+8R8+5b4HNqGJOiePz9PZGeXc2SyzsyVuvb0fz/Mol22e+d0RfvCT66k3moTDFlZAp+pKHHRils5wyCZRKlKcNVpK9wjnz2VbwLS+xhXEdTyEoTExnmdoKIH4kshlnwa21ElYOk3PRatXqJ94DWvkYRq6Aqga4DmS+YUKLz5/gmq1uex4YFkGj+4Z5qMPx7n9ng0ErRA72jSKWcHNt/Ty6kuniCf8eJ7k1IkFbNvlkd3DnDw5z569wxcBztXxsevRbFLtgSsCqI2ay0vPH8c0NBbnK2umOqGwj2qlucw5XqJlScdDSEmjauMVKly3Id4CzitCJVDPneM5g5IeY+eqZsqlImIvLImyurql/eIo0vWqLwzFhrykn3HVUbzUxy8QfhVqTTS4yKbxL7G+8eBUSonjOBiG8a8mWeF/1lri2AigI+bH+gqgFNTD8eN5NdoMGILr02r3eHBBsjP99c7d0iKs6bCYq2Po2nIncHGhugxANV2JjlY/v5d9EEMmzz97nEf3DBOJ+UEo2ybD0Hjo0WHmF8oMDaUoFOpksxV+/LOd/OZXhzh1YhGAn/31DWQyFXK5GhK4654h5uZKLC5UiEQs0u1hpQDXoFJu8Nq749x1Wy8b4oK0X/Dbs4pbKFHuBbanIkdTfoEFFMsOZ1txpm3pEN/pjZEzDJpNSamhrKJ2tCnO30jiwuOoAMlDvSuWMqn2AOGIj4mJAtL1aO8M07Q9Xnz+BJMTBR7ZtYVXXzzBwkIFv9/k+z/egR4NcXOHOl8lGyI+Nbpb8rNtepKKo0bCqzsPF44ANaGMs29MX/ra+SIjubtLjbt3D8D+WUnEJ5ZtWy53LTRqLrOLRfx+A7vhYJq6GqGixmyiWOPY0Tm+98PtFEuN1pj24i6PbWrYqTh6MoGrSz7Oe8RrSsD2zDnJv9kq1gXGq2u+DmGfWPZFjJqqq3ypzszqOpOXLNQlqYAaEf7VsBL83d65FphWHEn1CsaHs1XJS2Pq3uuPrL1GPv8TAtM3JxU3eXVXLx0Q7LwCgPrqhGRjDNr8ylO30nDRak36+uO88dpppiYL3HBDD0eOzPDI7i3Uqs014p1ctoY/YNDdHUXTBS89fwLPkzy6Z5hPP57k4V1beO3l0yzMl/n9b76guyeKzzK46ZZeDEPns08nadQdQBIImoydz9HZGWF+oYznSkrFBqGQRT5XI5etcfzoHP0DCT7+cJyRaztJJvxUKnUmJ+u88NxxgkGT62/swdAE8/NlEonA8gb5wpJSEo35mZst0dUfu7gz2RIFaR58t8dh8vQCxxYqtLWH6OqKktVvQDtXJN3rwx/QKeQaOE2PZ/9wFLvprbHiajQcXn3pJA/v2sILzxzjRz/bia75GD+fJRjy8eAjm2k0HJymSyTqJxKx8DyP2+8YIJHy4zSaGJa5fK+spvasrtVpbGbg8nQFIQTTMyWKZZv+/jiLmcqqn0Eo5KNQWNkIO46nRK4t0r7rSPLZGgND8XXvybil1PTPnWuyNWHib91Dq7m8lyvPk7w3JnniCp95lg62vPT9FDSWTOrXvsZnaDiOR/1q5/Qvv2zbZmpqip6eHizrEmHVV+tPUsmQuZz3vVBs0Ju8Ek6MUvLOVGGxJnmkXyynYyxtjC8V5/dl/CUNqJaaNG0X02fgOhK7blMq2TRsh7Z0cBmAmoa+nPqyej04+OkUex/byq9/cYh33jzHdTu6MH0aobCPYNDEw6OzM4pAEAyaaJpGsi3Av/tfb2VyokAmUyGfrzG0MYVlaWi6MvG2fDojI2l8loHrurz1+lmymSquJxGGhhYL03A9Ypbgr0da36f1QF+oSZ49L/n+ZsHCXIVf/KOyyFkqv9/kJz/fiT8d4vaWc0G2Ae3rZPnNVBThfvWYfynRy+pJEdM8yosl5ucqbNiQZOvWdj7aP878XFml7FRtnvrVIf72727BFzJ4oFcwV1PgdMnP1tU1ig2XqKlcGpYFa+sIhMIGjFzGMgfUOPjFMckPg6rLdyU8ygu7N3fds4F63aFUsonF/KBreFJF4VbKNoGgSawtwMBQfN3OZ8QULZ9c9dk3xwSfLShhk9LWfzkVJaCrrnVnUBL1qeN2Q1pFys7V4NaOS//+kjfwTe207GsUfzt1gYj3d2clG6OKu701wTpxv5KTedg3I7m982JgOlGWTFYkjw1+dVrUhcD0w1kldnpoHV7tlQDUUJeKgxwtSspNyZ6uIK8+d5Sbb+vnsW9tY2a6RKPR5IGHNpNvxZLGE2EsyyQa9ROO+JidLjE3V2LzljQ//On1zE6XqFRt9jw+QqPh8JOfX0++UG8BTZNUm+KLH/hwjJMnFtm1Zxh/UAkkbdslHLYo5Or4/QaJRIDTJxfxpKKL5HJ1Tp86zSO7h3nhuWPsfXwbszMlXnlRibbMrgi1ik0oYjE7U6SjI4JxCVstn6VM/BcXKvQMxJYB1lK08tRkgZ6eCMVCc3k90HWB60r8foMf/uR6Th6f5Z33p/jWk9eSz9XILFZpNFykvBgQ1+sO1YoSAM5MFQnH2/AHTN59+xw33dKPrgkcFZ2GbTt8dGCc63f2YFkGc7MlOroiROMWch1qz+paSmPrG4oj5fr0A1dKzhcEmYUa0pXkc1U2bUwt/1xKqKxK7AM15m+s6qzqhiCevBQXVnK+CCczNpSzzJRSbEj41nvhJUvTBEJcOYfWp1/ePUOiXG0urETIx0LBJfUvEi/2562rRMur9WcrIQRBn4ZAcn6xineFhPeFlnHxkm3SUkV8gr/ZqpG6YE+hAc2ay8S5HJ9/vJLgc+HF3qi5NOrKBD+brVCtNqnVHHyWTj5bp7c3jt9vcuDDcR7etQV/wECIFU6k329w4829fHxgnN2PDXPjTb3MzZYwDYNkMkgyFWR6ssTERJ5UKki1ZvPpJ5P8x//Xu/yf//E9xsdy3HhTL36/zuJihTOnc7z/ziinTmYwTJ18vs70dJFf/+IQX3wxR73hYvp0HntsK45pMN0Sg2lCXNRp+qsRgVFzLgKmAPV6k1/+80ECDWf5oVO0VbdpdXlIjuU1fjpiKmB+QUnp8eGipGcwTjod4pOPJ3jh+RPMTJfWJkcVG0yMFwhqGgMRwabVNix+nXseGaEjomNqK/zI1QKh1RU0WM6UvlR1BAW3dQh+f1bBwCupC7s3R4+o8b3PpyueMao7KExt+XMtJZpdmXBDeXjGfKrz2biE3+jqWhICPT+q3t9pJc/sGdBoevDquMd4WVKyJe/PSL7IqFSlbEOSaSjf0oOLl4vwVW4NKb9YVh//80m55r6sOAqY7mwTy131JXGMEII2v1IXL/2ZrmuYhvalHP71gKmlQ8zHJcUiCqAK3ppsieZWH10hSPg17ujSeLRf47sbNYyAwR13D/GbXx7mH//+U06fWsCTkqbtAqI16m1Qq9k4jke51CCRCrJ5SxoplYn/u2+fY+xcjs8/myaRCHDsyCz9A3H6+qL0Dyao123OnsnSP5DgBz/aoQz4heCDd0d5ZPcwhw5N43mSYNBHKKy6d4ah4bmSWMxPNlNlYb5MJGQxNZGnXlsZPVcrNj6fgZCQSoX49OMJ9j6xDcta21Py+w127Rnh2NHZNZOGpZjh//L//Ygjh2eRUrsImIICmk/98nO27+xFN3TGR3PU6w6FQl1NFi8ReFEo1LEsnWymiuk26Gk3KRZsnnv6KO+/e56jX8zy5uun+fUvPmd2ukRbW4jpySKffjLJ3EyZWsVZTlVbOZGt9XXVVH16oUa5qREUNs3Jw/i9lcF41VEpdAJJT0cQwxDMTJdoaw/h968cp6WAAMsyEJrAMPSVVDAhCIV8dHSE172Xj2dVzOgt7UqIl7uSufw65dMUPeDLS3I8KxmKXvoVAoG1Tmtxif5k/onTxf5H1De+c3q1/ry1rTvMJ2NFpJScnCmztXud3Mw1JdieuvwrPlmQbIq1jIm5NH/pkb3DpNrVbFcIQSFfo1538PlD2A2Pht3Ab5kqT9vxGBvN8JOf7+QX/3iQ/fvGuOf+jSAlE+MFEgk/lmXw0f5xctkaW0baGR3LMjzczof7xijkawxtSnHDDT18+skUM9MFrr22i0jET1d3lPb2MO2dYWzbYXGxhtN0ae8Mc932LrKZCo16sxWfCdft6OZaT5JuD9HdG8Pn16hVJBNllVW/XgUsndET2YuA6VLVqk2mJgs0u9OAx7YEPDcqiVvQGxIYQLnkMlDM8sk7ig7Q2xdbZYAveW1CsmdQdacnJ/IUC43l6McLa36+THVz6qIV560Jj/5okB/8aMeXCoRmKhIhlJPAl9XWpGCsLHnmnGTv4FpT+k/nPcI+GG45BqwnzMpmaxw+PM3uvSOt5CBFX+jo+GoijQurzQ/zNXj2nMoUv3+dLiGo7t+RrCTfUPzJ0aKk4UjGWhZfd3UJJsvw2YKkaMM93YJSU8XySiBlCe4Ygf92QjJZlvxo88XWNUezSg2+ZRXPeGebAvV390Ctqa6vv9mqjtOlRCu6LpifKiKEsg8qFurEkwG6e6IEQwYX4vD1gGnCUi4MN3wJPefCDqqlKcHO3FyZXLZGMhmgvTOM5dcJWxpa0s/P/+ZGzp/Nks/XcV21FtRqDrqh0ahDqdgg4DcZGExw9Mgc9ZrD/n1j7Lyxh+mpItMtO7c77x5g05Y0n340zpbhDt56/Qxbt3WQSAXwHI9K1SYctqhUGuy8oYejX8wwPponEDDY8/gI7793HilVF83n0xV/tNggm6kSjfupN1wq5RXkUyw20A0NT0JHR4iZ6RK27fDonmEqZZtCoU4spmzKjh+dpVptrpk0rMQM2+zeO8zYWH55PbgQg9VqDgvzZYY2JfFZBoV8nUhURXUa68WKAbGYn6mpgnKuKC9gjb7P93+8m1/90+cUiw2KxQYgCQRMvv+jHbz3zjnaOyLL1IZm06VvIE4yFQQBniuXI099Ph3d0HBdj46kH73e4PCpIpnFTtLlMj19OtJv8NKYEsslLGj6woRCPsolmw/eO7/GUcTzJB9/NMHux0Y4sG+MfKGurmkhaGsPsfuxreve17mG5GBG8r2NAs0T9IRgqvrVXQQ8qZL8Di1K7u25+BqXSF5tBaK4nqLZRC+j6Ae1UXU9yWLZJmzpWIbW4ppKIv6/fGj3l/8NrtZfVIUsg83tQU7NVVksNSjVA1/pRpqryVb8pWRzTHUM7+4WPHde0hkAreldkr/0+2dO8NOf7CAY0tEENG2Ppu0yO1PEsgw8aRAImpimIBAwqVbBHxDKmmkyT2axwsBgnLvv28D8fInFuQp33j2IP+CjVKxx++0DaLrGpk0pcrk6Q0MJYnE/d9w1yPRUgTOnF+kbiDO8NU3Tdjh/Lkej4TC4IUm9rvhv5XKD9o4IC4tlpiYLmKZOqi1IKhWkVrVxmi4+v0Z3SCnoh+Prx/CZmsHil8SZLi5WuGtHF03bJumH7hCczEkGQ4KZqYryS10FbgN+ldzU1ROiCbQF4MCspOLIVhjBpRftZDLI9FSRTZvjVFvm9+WmMpe/o0timpcXCFUcyWuTkm8NXl64s5rK8Uif4L+dkLwzLdmZhnITFiqSkwXYPaC6GPM1CJiC+cXa8qjdQ1JsSHKzVaZfP0tH0uLmm3oZ3JjEcb2vDUwBpquCzXHlk/jyuMe+GZVvbmpKndv0FA+40FRArS8sGC0qX8qUn2XTcVBRlb1hwdPnPI7nFOXlQqrA32wVvDbh8Y8nVYJObwiqLsvJYAFjRYCWbyjeb8iE8ZJKEEoHlS3XpTZ9mia4+74hdE3w+998Tr2h7HmiMT9IyZM/2E5nd2gNQD24oADFamAaNKDkyMvmyS+d2/agYGfa45N5GNZqvPz8cSplm6bjoeuqC7bnia10dYcplRoIobjNobBFJqMs2jq7ItTrTYJhH5ZPp153WuuAyYEPx7jhpl727xtTxyho8qOfXE+53OCj/eNsHk7jei733r+Rqaki5VKD9o4w7e0hmo7yx23aHu0dETZtSdM/kOCzjyeYHC8Qj/up1ZXH8dL7J1NBDn0+Q1tbCD26lndx4MNx7n9wI7bt8IMf7+DN189ww029HDo4jWFqnD7VxDA0giEfu1dZkS1FKy8FCui6Tmah2jqQFx/b9o4wsXgAidqYRaMWbW0hxkazzM1WaE3ol8vvNwiGTDzHpb0jTGPiQyrHXkVLP8TP/82NLCysuHi0pUO8/845ThyfZ2RbB2dOL+L3m2i6xrtvneP+BzcigGy2ivQkuq6h64JSqUEqHSSZCPKf/q/9ywIngSQUtvjuj6/n5o4Q54uSk56kL6yz+7GtPP/MMc6cygDw87+5kcWFCvlcjbZ0iL7+OL39MSYnCmqtTV/a57TUlLw5KfnWBjWxczzoCgtGi4o6lQ5ceXdyqgIDUcHpvFovL4wnFQiG45J0UG24Xp+8eC0t1R0mszUCPjW16RIOH5xuLp9O09DwXBdTF6S+bLz0F1BXwenV+rNXZ8zi9FwVkMwXG5cEp6NFyWJdjfo2xwWV1gjnhrSg5sBLY5Ib0tAVElybknyRg87ypflLzRZ/aWiTAkCmT8P1dCrlJp2dQWq1JsViHc911UhIgOMIoMnmLWk2bfLIZqpMTxbp7ArT3h7m/LkclXIDQ9d56leHlZH3g5uoVGzmZ8qctjOMbGtj+/VdlMsNTh5fwHOl8k4MmoSCJo6jYv50XeAPGDSbLtKTRFo8uEjE4t23z3HLrf28+tIpdj8+ghnQ2RxXnbJb1jGlb3rOl8aZtrWFEK0+aKYO0xXYMyiolJ2LgClArUUH+Lv/521YYaOlFJW8Melyd28cy29SrVycehQIGKTTIU6fXmRkaxvY6jVhUyVUhVvq4ksJhCSSzxfglnZxSX7xpbp6O9o0Dmck4yWV0+7XwRTwwij4dWV0HTQkkXiAog1GK288YICugytgJmvj6jqO98eLDOK+JbN7FaE7WVI8y4YrWay1zPQ9ZX+1xPNdanh6cv388SeGBP/tuOSjOZYtxVbXw30aJVttBPbNSnya6rQORBRl4M0JRX8oNeH6tOCmtMBvtlLTWhuFJdpDpeFgipXztbhQ5Z03znDrHQPUW+NoT0qKra7eb351iH/7d7dghVbucb0l6loCpptj8NK45M5OwTvTknu7WSPaUnZGDrOzZfLZGslUgPbOCNdHJb/+p2NkFsq4jrdM4CgWGjz3+6P84CfXEwj4+PjAODtv7GVxoUIiESAU8mH5DE4dn29ZtqXw+QSFQp1wxOI737+OzGJlxc+0J4ZhCH7768OMjxU4cXyBRDLAyNY0t905QD5fZ3ayyNnTi9x0cx/VSo16vcnQUJJkKoCmawwOJUl3hPH7DRxHsn/fGPNzZXVvtIcpFerohoZuaGuSrObnyrz95lkefHgziZSfb3/vWnKZKo9/exulUkNxKZMB9HiYw3Wdm5qSkCkwdY2FVsxwqi1IJlshlW4pAi/gzbd3hLntjgFefO6Y4tgHfUhP4jgeT37/Oj7ar0D0kijK7zd4ZPcwp0/Os/vxrdRqNu19O3FlmEjUz3/9vz8jngwQTwQ5c3qR6akioOKH0+kQ01NFtm7rwNAFxWKdxYUKd92zgeeePkbTcwkETQqFOun2ELv3buXZp4+uUd5LBJWKze9+dYi//ttbyGs6CQuOZDx6wgF+9DPFFV5YKJNZrNA/EGPbte2gCRzPwycM2gPt6HXoj106ACFiwpMb1274PKkCPb4q9usLC/pQ1/un83BP98WvGYxqre8n14SrlBsOX0wUcVwPcQFNSUMu6zic1tRqe28U8xLd7r+k+saDU8uyGBoa+h/9Mb4xJaXk7Hy1JQq5/M4zbkHDg8my5HhOqXi3pwRDUdU92xCVvDMlGS+rzPfZqrjImmh1NVxlTaQJZU0Uiwcwy02CQR8nj8+xabiNSNiiXG4gpRpbLS5UEQICQR9+v0myLYTjlhgfz9PXF2fbNWlKJZupiSIj29pJJoM4jsfcTJGevjie6/H+O6MEgibDw+1s3dbOKy+e4trtncRiAcrlOvlsHRDE436aTQ9NU3y4js4In382xe/3jfPo7mEaDYdcbiX+L2p6LF4ivtW2VZyp32+uO9oPBE16+2LYrS5mOiD42TD4TZ1TR7IXAdOlqtWaTE4U2HJtWrkXoDKm/YbGD3+8g1+u8haEFWP+9945x7Xbu3Cc1alSgoBx+RGZ60nenFJ58QORSwPTy1nRkApwJu/hSOgKQV9EmVeHTThTgFzD47p0GL+lY9suIVON+wQqTSvk10m0R3h1TNlABdd0PdS47lJpLxdWbxieOS/J1CWDEcWnDugwUYbOoIoYLTXXCtAWauqh1rGOYE39XNAVuvCxtbYiPsGIT1mCZerKMeFIRrk8bE0Ikn6oNOFIFizHpZopUy/WSKSCdHVFmG/RHgoNsHRJxBQ0m67yFq02KZdsolGrNcpVANV1PeyGw9REgU3b0niehycVlWDfjIrOHEkIzhcl/RFB0q8U0TNVGIqq8+rYHnPzFV549jiu46HpGk3bJdUW5Jbb+8kuVHCarSf5MoiXzM9XmJ4qMrw1xfBIB//l//cRff0xNm1OYZoaJ0/MYwV8JFIGxWID6bkMj7Tz+98e4dk/HGPzljYGNySYnS2SbgsjhM4ju4aZn69QKtZp7wjjuZKXnz/B9p3dBCMWTcdjbDTH4FCcvoGECtIYLxBPBtkwlOTYF7O8/eZZ8jk1Ul66N45+Mc2eJ7by+iunaTbdZXurJYCaWazyzttnuePOQWJRi3PnshiGzu139mNY2vIGopZX3GPl+SqXY4YzmSrlot3i0Ktkt9XGNLfe3s+rL51UoQQNF7CJRCwk8PvffsGPf7aTXLZKsdhY9jl1XY+Hdw3zzttnuefejTjxGEXZzUBY8IOf7uSpXx5iarK43G1dWQfO4vcbhCM+PE+Sz9WZmyszM11k92MjrQ64i2noDGxIUCo1mJwoXHQ9Swm1qs38TIHrt6prazAK/3DcI9Gv0b8pwYYtKvrP9SSOlCyRcatNl3cm1aav2BCXoZKsnUMYhkkj2EFCrt08fVl5UmI7nnqm6B5g4CHR1n3+Sd6ZlLQFVGDNVK6mQmukAqZL43vJipfp5o4gPl0jV20S8Rv/Kkb6cBWcXq0/c80VbabyDXVrCkHPZTKA45ZK6BiOCxqu8jfdHF/5edAQ7BqAY1n4dEGB1/74pQmJfh0isRXBgBXQcR2Ppg0dnVHOnFzkuh1dhKM+qhWH+fkypqmTSPhxHY9KuUG53CAc8pFMBvE8j4X5CvNzFaJRH7fe2s/sXJGzZxZJpULK93OqQKotTDBosrBQ5vVXp9k8nGZutkSxWKejK0JPT4x8rsrsTIly2SYcsQgGTU6dXMTym+zaM0I47OPjjyaAlfg/UIKVz+Y9AqYaeXYHFRCRSEJhY5kzuxqgBoImP/7pTqJhY43nnqkpXuKX0QEyixUMrR3XVbn1k2WYrXp8rz94kTH/0khvbCzPrr0jyyP9pdIF/P1x9Wc/2iwuWvTfnpYMRQUdQai5ULAl7YG1VkNfZkXz5I+2syGq8dmCGsV1BgV7BtS4fyQhCPs0Jhs6j+wZ4YXnT1BruuiaegAG/Tp7H9tKKqpzgyl5eUzZFW2ISkZLyh82ZAju6LrsIVuucEvku1BTWKrmKLB8U4egNwTTFcHM4lqYuS0JhzMKgJSbK53mpW5xfqJEYLFGb1eAZu3SnNjjOY/ZiqAhBY4r6QsrzqtACZpCJlxXrPLS08cplm1cR+IzBf0DcTo6FDc8bkG+oUb+PkciNPVehUKdYMi3DE4BXFf9PJOpMqyB4yp6QqWpNpmDUXWdHs8pj1L1/uqe14DsorJcevHZ49TqjvLzFBCN+TFMnbmZEp7XsndbrX0TChBkMhXm5wNE475V1Jwq1VqTa67r5Py5DG++doZisYGmCe5/cIgf/fR6FXe5WCEa8zO8tR236VCteLz5xhkCQRPLpzM6mmPHji4yi1We/u1RLL/B9us76e6NMjdbYW5ulpGt7WzckiKfrVKrN+npT3B/wCSfq5FMBenoiNCwm9x8az+eJ7ntjn6ef+b4cpJVtdKkUrHpH4wTCppUa01eeP4E5bLN93+0A8MUywIYUNOlqYpyhLizRy7HDE9PFonF/Hies2o9cJYpEPVak1BIpWFpmvozIdTmzvM0MosVXNejpzdKLB6gaTvUqk2e+cMRHnhoMz5LJc4NRBVQ7uwO8e/+7hYmJwoszJdJJlWE8/vvnGP0fI5de0aw/AZvvXEWx/FIJAJ89OE446N5OrsiDG5IcvZshkDIJJupXvJekijgPqypzHuB4CdbVNqZ63kXOUuAcnx5cUw9R27v1PjDOY/NsUuL8C6ssZK6X79KHZsqkVs1UQr4dKZzfgpVlfTVnwoQXqVuilqCpOlyaKKI47gIJIYmGEgF6LuMw03A95cdV3phfePBqW3bLCwskE6n8fm+mj3E1frqZbfmFT5D48YNCQxNPaCkXAs4LixLXzJeV+V4KubQkyr1RgBtfoluhbE1DdNzL+Lf+f060fYVwYAHBCMmnmMQCJlE437On8vh8+n09MZo7wiRXayQWVTjrPPnMtxwUy8fH5jk2NE5UukQj39rKz29MfK5Gl8cniGVDnLDTT1ks1XOn83SbCpOludJ3nrjLPNzZcZGc/z45zs5fzaDz2fwxcFprr+xl3LJplZ3CId9CiyElXlpJGLx2aeTy13heCJAti45XYDd/UoYU2v5m344q5TMdRdqjmTvBhVnOjWp4kzb2kL09MaIRdYC01JT+V36GvJL6QCpthCOpxJ9PptTqVQBA4KWRsaRfP7ZNOGIb3mktwSGY2GDZ8Y9cg0Yigpu6RA82q9RcSRPnVag5UK/zY1Rwf45SdBUategIXh7SgHEm9vFumKmlZLYtstCq9O8I+Xx6qTkaFZye5fG/e2SfKHBwngJUWoQ7Ivx736+ncmZMtlslXTCoqsnRiBo4AFtAcF3NsLHc7BvVgm0oj6uGJiCAl0bojBahAf6BP98UvLt9qXRoUo+67qAy3Y8B5vignpTcmBO8mAvaIjlbnGt4VBqCs6YELTWNy7P2RBwPdorZZxyjVQyQEcogoFGvUWHCIUsnnv6KIuLVTXqlFADJsYKdHZFkVJiCEHcklSaUo1JXUnQFMRifk6dXEvp0HWB40hSqSCOq/h7VRdu7RR0BJaoAXDHBd6rS968r710io2b26i3gOnS64uFOoODCSJRS3XmLhhTL1Uk4lexpLMVPM9jbrZEZ3eUaMTin//7ZywuVJY7e0LAG6+d5dSpRZ783nV09USYnSlx7owCSaGgyY039Sr3iXKD4ZF2kqkgTzx5Dfl8nUrJJhzxoQmBZenccGMPTcdh9FyOfK7Oxk1xQiE/9YaFZRkEAgb+gEE05kPikVmsMrQxyd/87c3MzZQolhp09UTo6AzTbDo06p7iiN+zgZ7eGMHwxUIzgPt6BM+OehxZ9Ni4Kmb4/ffOceddGwiHDf7uf7udyck82cUqvf1xFubK6twaWguYKopDvEU1qlabbBhKsLBQxbZd/JZBo+HyxLevRSDxXMnkuSzzCy06TXeUidky9UaTm27tJbNQ4ezpRTYPp7n9zkHK5cbyWtjRGSaValn2BU0WMxUGNyRYmC8vuxRcqgSQSgXXjMAtXYmWThfkReB0saamMAFdNTxA2Za9MSnZM6Dsmy5XTafJ1EKBOztiwJXN9W3HI1exEXjqEhXQsD3Oz7so52FBptQgGjQZ7gxjGRpbog4npss4joupQ28iQEfUh9/81wU+v6y+8eBUSkmj0fhaOb5X66tXqaZGVQ3HW1ZijBVVwstdXXI5c/3LStfgmqSy16m7go9mJUk/DCQNHn9shKefPYGFuzw68fl0HtozzL6sxhOxFY9JD9AMQaXgcOLoLH0DcXp6Y5SKDQqFOpWyTbFQR2iCW27rQ0pJZ1eUg59Nc/5slldfOsV9D2xCCOjsDhOPBxg9l2NxoUJHV4S+/hiGrnH2TJZrrlMcts7OKNVqg76BJK7jsn1nD9NTBSy/SVt7mEK+SijsZ3w0S09fglyuxtRkcfl7dHRGeG3G45qE6nSuVuxfl1pZYCWS03nJWEmjszPNNSOdBDUXTXoXpZRUmorD6zccnuyLEfCb6472AwGT7t4YxxYdZqowEFERod/dqFEH+npCfPu71zA5ocDw7XcN0tsbIx4xWGiqHHRXwtbkynuGDMHGmIqevLAGo6rDdmG9MSl5flTy6IC+LpXDQ6nYQ4ZkYr7GWCDOqZykN6yskxqFOmcWy7zywsllrqShqZztbz15LZs2R1lcyKD71vLRBKLF8f16Vi2GpjrcP96iwDasRLZWHDhTkPxseOUesD3JdEVyd5fghVEImmojYjRVV7jRcHCbHqbj4aHRQF5kXK4BTq7Ki384QVCsbNp8Pp2HHt3CJx9NUKnY7Lyhh7lZBVR0XUMTSjlcKNaVpVHraxtCELPAcXUqZdWJD0fWdk01oWylfJZBT2+Ml8Zc7AuA6dLr4qsszPzAzFyN8+cyq1KJ1q7NUsL8fIUbb+nF7zeorpOG4/crnnOp3CCTKXPqxCJ33rOB9945h64J7rl3iHffOcfiQkXFEbf8Pm++pZ9Mpkoo5OOjD5UTR6Vik2oLcfudAwQCJoGgiT9g4jN1qtUmvb1RxkbzuK6H0ARu0+OpXx3i7nuG0HVBuj3E6GiR9naH3v4EpilwHCgWajiOxtEvZvhw3wSP7t5CV3eU62/sQhganuNgOxJTGoRignRXSCXsed66wHSpHhsUnM1Liq7a4C6Jggr5GuFojFq1SSoVpKc3BlLSqDvU6g7BoAkSPOkRDvvI52sEgz78foNYPEA8EaCQr2PbLolkQHHEXQ9Parzzzii1qoPtSRLtEYZ6Ihz57DwbBhN8+ME4t9zexycHJnnnzbPL10kgYPD4t7aRy1axLANd1yjk6oTDFo26i+tJOrvCy1SECysQNOntDavu+aqyXbVRX12elHy+qNLwjmTgbEFFhEZ8gq1JeH1S8kjf5ek581VJTP9qyUsTy2uTasLcNBBlJt+g1vTQhU6t6VFuuJSqNh+dy7VeqV7tM2Bnf5TANwyULtU3HpxerT9vpaM+FspKYfjJaJ4bB+N0hQT3d8NoCT5f9NgYU2k+bYFLezQKxBpS+u5Blbse80n6u4L85Kc7mJwq4ZSVvUxHZwTP0sme9DiTh1RAGbo3XDAcj2d+9wWzM2Ue+9ZWnvrFYW68uZtbbhugUrHRdEEk4sdzJUJINmxM8Nf/9ibm50qUSzb1uk1HR5h6w2VqSo0at17TQSBg0Ki7OLpHKGySSLYRDvvI5epkMlViMcUxPbBvjIXWKN00NW6+tZ933zrDtdu78Jkan306BaxwKKeaOglLMrAOaLvwGG2Jw5a4ADyyDZdXJuWyp2l/RBnFm5qK5hxJQLYOobDBj3+2c60oSrZUyz/dyamGQaHhcVeX4jmOn5XL5u11wAobXLcjjWEojmnV9qijjOk3xWH/nOIsri5Tk1ScKwV8iud6OCM5lZPLKVNrX6G66gUbEokAyVantelBo+qxOFXhpedXeH1CQNNVXMXnnznKd3+0Hce9AjPSr1FKDCF4ftTD0OBETo3uT2blRZ3jF0clPSHBm1OSeOthGjQ1JicKLfBWx/O8ZYCraRqxuJ/52RK9Q4llMdMzz57AlO6aFLxGw+E3vzrEo7uHGRvLkVmsLHucOq63DFABPj4wzkOPbuHdt89RKDVwHInP1OhoD3HXvRsw9JaQp+6gaUqtLyV8/0c7OFE3aLoed3YJUv5L3M9C4BOQLzR55aWTpNtDVCs28URg3USkQr5Gpdxg92MjPP/McRq2s9xB9fsNdu1VpvnRiEXGZzI/V2bfe+f57vevY/R8lmy2yrefvJZisc7CQoVQyEc6HcLyG/zhd0eIx/w8snuY0XNZSiWbUMiH60oOfjrFg49swnUlv//tEW67sx+74RBPBGg2XUV5MTTuvX8TjuPQ2RUll6ti+jTspkdmsUKl3KBvIEGxWOfEMSWu2r1Xeeq+8uJJtgy3MTiUIp6yljdGUkrW0+M5nhKJbkuKZU6yhnKEqLuCYl3Dp0nCYR+6oeM5Ep9PJxz2k8tXGT2bpX8wQTIZwGnxIgUCz5NEIhamT6enJ8bJ4ws0my5taUVXymWrhCN+BJKZmSKNukOz6YKu49VtDEPgOh7HDhzljru28Norp9h5Yw/dPTEKhTqJhJ/NI2lOHpvj6JE5dj82wgfvnadcbnDo0DSP7hnms08meeiRTWssoZYqFPbxgx/twDf6Mr6Rh7CNlQ6rZUiyNeUGskyBEYKH+gRNTzJRkexdFfs5HBdk65I/nFMTme2p9UGqLi7Ne1+virUm07kaAo9kyCQZMglbBps71t7khVqT49OV5VQnTajAms0doa+covivqa6C06v1Z622sI/OaJO5ok3TgUzZpjPmZzAmGIwp1fK5okqseWtKkrAUmAqb0BEQ9IaVqOXCzpWpCR4bhANz8Mm8x+1dGj1DcQ4txilISVSH0Zzklg44uAB+U5KpQSoguN4pMTtTJhJZyV/e9/44qbYwX3w+QyzuJxT2EY50MnouS73usGFjUllANWwiYT+zMyUQ0NMTJRg0KZfrOI4axS3OV+jqiZBMBXCaHs2ms9xZ6uoOoutKyBUK+4jF/DiOywMPb8bvN8jm1LgvfpMC2EWh8emUd0WpRxdW0lJj6aarum+nC/DimKThKbugbB12tqkxfVePogNMTij+XbItRG9vjIZl0OlIdrStLJo3d8A7U5Kb2pWzgiaE4pbaFyv3ZyrrmP1LyVhpqdMLpaY63ym/sn9qD6wvQPA8ScmRbOmMYPg0bNtFoCyZKk3wG5AMGmzsi2CbkiMZxVvtrFWolBrY9tpujBBKPFEs28zPlghGv07HQnI020o3EVC0JTqKRxY0VddPSpiqSBZqShE8VlKg9FiONdSVuivJNeC+HjhVgI0xwVhJJUxlMjUK+RrN5lrE4rouhXyNTLZG/8YEHor2UK27RFezloRyr6jVmpRLDfx+g0jUWvoKrX/L5cSJhQXFO7z/wU3MzJaUGj9qEYsHSKdDLCyU+cnPd1Is1CmVbKJxP22dUU41DHoCyrJrNTBdsoVCQr3qUC43aEuFmZzMk8tW2bS5bVk5vl53VNc19n0wxq49Izz+7W3kczXy+TrxuJ9QxCIc8mH6dErFOqGwr5VMVOa1V05x3/0befP1M3z68SQAff1xQmH19zz/7HEyixUW5itUqzbf/u61zM2WmWopzreMpPni8Azbr+/mkd1byCxW8XRJIhmkVKorkNseolSyyedspFekuydGKGQxP1+ikK+TagtSyNVwHElvXwzPkySTQZ75w1Fq1SbpdIipqfM89OhmzICOJxXffqGmnBzyDUmtCT4DDKHoJvtm1VRiJMmyTZFfh2DKR6Pm4jiu6k6aKvBjYb5CJOJjxw3d6DrsfWIbLzx7bPk4m6aGYRrsbfl/dvdEyWYq5LNVkCpgpFRqYPp0ahUliCuXbfXdsqrzrPksjp8qogVn+c73rmV6qohtu4xsS5NKBTmwf5z9H4wTCBgEgyYPPboFu+EwM11EE7Dn8a1kFit0dof5d//+Vmamistc9nR7mEjAIf/L/zft7Ruh/drla2NrQiPmk8xWYVNs7d1ZtJVFW3KV60fTUwlQewYEmTp8MAP39lx8Z7cFBOJKQg1bVWhNCf2mxvbeSxNVYwGTmzbEqDddfLqGoYvLUty+KXUVnF6tP2tpQhALmswWFXCpX/BwtfSl2Ee4ZVU8o2xFyJ0rKpHMNUlly7OUKKqhoi9HElCyBUdbaTkVR3VIf3dW7XwjrXxypAJJA1Gd+RMlpJQEL8hfPvDhOLfdMcBrL5+i0XA4dzbDPfdvxPMk2WyNqu3R1hGh5kh0U0cAxWIdIaBYUr6lqbYgnV1RqpUGn308hWnqxOIBKmWbw4dmuPGmXs6fzxIO+XA9j3R7mHjCjz9k4kpJRzBKV28UTyrR0Rvjkod6lb3O1zr+CCxdpfHc3K66iauP8dLI10F1QLdcm8bQ2nE8NcLzA7ELxANDUfVbr09IbmoXbLhMsompK/7k6lqoKbAc0AXnipKAIVisSc4UFKDZPyvZmlQimtV1PK8ibFMBnVsfHOHlF07gNF1MTZ3bgKU6aLpf5+VzHtvbBAN+jamFJrlc7aKOnOI9g+NIFjN1NlyIoq+oBN0huUxROLigjnW9IIn7VgQ/S0KW7W2CD2Yki3WlYr951TX/0Rzc1C44klX/3hwDzxOcyEE47LsImC5Vs+kRDvmQCDQhyWVrLXbb2uPnOOozFApqI5XeEsbytWyMxNK3AelJ/AGTYNDHm6+fxnMllt/k+NE5CnkVzfnYE1v58INRQmGLSNjH0FAXwhTcjOStSWXuD2stv+p1h0jEz+uvnuLJ71+L5ylrqqXIYMsy2Pf+KLv3jvD8s8fXdLkDQYOdN/Ry4MMxHnh4M76gSShi4bd0kqkg/rCPuekSb791jvse2MjuvcO89MJJxkfzHPp8mod3DTN6PkuxUCcSsUi3h0gkA9x93wYFgJJBurqj1GtNOjrDmD6dxXnVFb3mmi4sS0PXNAJBA6fpUanYtHeEyWWrTE0WMEyDRs0hkQwwP1emVGrQ0RkhGDSpVm1Mn8HY+Szlss2W4TTPPn2U+bkywaBJPBng1P5FJiZLTIdjTJcl3WF1X1ka+A3BJ/NK1NMTUsfVlTBbhWfOSTbH1NRpQxQ8BGZAJx2MoAkF6s+fzXDuTJahzUkqlSb9gzFSST/f//EOZqdLZDIV4okAnR0R8oUqWlFgNx18rfG+5Tf46MMx2juiWH5llr80qm82PZpNl33vj/HQrq289/ZZjhzLc+RYnp6+GENDCZLJIGdOZwiHLL77w+sIBnxUazYd7WH8QYNQyMf8fJlspkpvX4xPP5ni3NkM/X1x0ODkiXmVhFUbA6D4zn8m+p3/sKZ72h1af31M+S+O8JXA44OCiE/dm0Oxr0bx85CwjmZCStmyfhIqsvYya7ahiTWiqKt1FZxiGAbpdBrD+MYfij9bzRUagCBoGfReRq2/ugSCoaiymLkDOJ1XHSoh1AOv1FRABVB8LKnG1WED8rb6b1CLeLm5wku6JSgoRRQIqV6Qvzw/V2b/vjF27x2mXLbJF+p4niTanSDi0xFIyq7k4ILkiaEInlRAYuHsLOfPZKjVmszmbAa6Q2wYjJNqC1Es1jl7NkM6HeKhhzczO1dCCEEsHiAc9vHy62col23+6qc7MJciMltr5QfTCsC0B6981ON6SuEdtS4GJ+sd44t+3/W4kun2hqhgw5fQDEBloHcF1/5ZOqg2GmcKkt6w4NoURMy137G5zmcIm7A1DtNljzt6gvzdv7k4Zaph6Dx9ziMdUAEDIdNjJG4ia4E1xuJLwNST4DMEgVgAf8CPpn317mnCEsvd4R9sUg8eu2Wu//GCZL6qaBR/NSI4MAsdQfX39ocFQqhRuuNJZiuSWzsEvzgl2ZZQ3ZTr0yBdWKhYa/wwV5ffbxCJWCrf3FW0h/XOzFJOeyzm5+MjCwxtcdj92AgvPX9CjclR59/vN3h09zClUp1ctka11sSyDEotQFKvO+RydaanSvh8VSplm46uKBu3JPEQbIjBdFW5LCxbfjVd7rx7A0/94hBdPRF8psH8fIm2lhfngQ/Hly2VDh6cYu+3tlLI1SmV6vT1xwH46MA4Dz8yTCRkIP1RunWBpSsF+4mcx0A6RLls89YbZ7nvgSF+8lc7WVxQMcVNu8k113ZQKNSZny1RqzWJuH76++MIoKMzwthoDtf16OqO0N6Kw1ycrzAxkWfDUBLL0ujoiFAqNZibLeG5Hm3pEA3bJbNQJZYMYBg6jnTo708QjpiUSjaL8xWmpooMbkii64I3XjvD4oIyug8GfQT8JoV8jXymzF1bEhfHOQG3dkj2zcplcKoLJQR6cqNK2pqqwNtTko6gZEtcsCmm1j71mBMc+WKWWq3J9uu7mJos0tUVQROCru4wG4aSNJsOTccjHg+iaSA0iEb8zC+UGBvN0d4RVR7NrseBD8eXP5fjuAQCpkqEq9o8+f3rFNDM1kBCe0eEWrXJRx+Ooxsa7e0hrtvRhWO7nD2bJR7309kdJt0eIputMnY+x7ZrOujujpLNVkmlgnR2RRAaZGaT9P7g/0C6DXyax8VzmisrnyZYrYW+1DqpaTrRaOyiNeHzBUhaF6f1RfwGEkG96bH/bJ4bB2Pf6DH9V61vPCLTdZ1I5Ct6Q1ytP6pqTReJstAwdA1PSmounM5JNsbEZW09mp5k/5yys/FrqlvqSOUVGTZVuk25CfNVNd6tu6rb0PAUUBVCJSF1BpUIR2iSQEcEyzLWdGyWHvoL82Xefec8AmjvCNE/EMcxtWV/zs8XlIVLrSn5LCO5NikoRnwszJepNVwMoFKs06i7vPDc50SjFu0dEaYm8rzy4kmCEYtw2EdHV4T39o0xl7OJ+Vj2Ml0SgyzUlDdlzILZqsSns2Y0tV6dyEk+nFWWQUKoDPQrKdeT6Ffo2/lVanVndnVpCL6zUVBuSg4uwm/PSL63iWVe6sm85PbOi3/vxrTqqsYsNcIkoFKm+kgwXpLsy3qcK3psjCkBxD3dgo0xQbMWol51CPgNanVnmZ/qSdA1gT+kPGb9/i8Hpk1PdXiXsukvrCXumk8ThH2qe9oXhmuSGlVHOR5IUJ6fIY+JcwVy2Rr+aIB70xGqjkbEpzxBQY3Dq+U61ap9kR8mrBikV2tNhJBIqQQqAUsHuarTKsE0dSXwCVvYtSaffzLBDTf1smuviscslRpEIhbhiEU06uPlF0/iuhLpyeVc9qUqFOoEgyaVio0/YDAxlqOvP8bsTIlSpkY4HqDkC/HC8ydoNtSxL+Rq2LbDwGAc3dAoFRvLXpxLG8MlS6VCrkZ3b4xYrJ2pyTyarvHk97YTjRjYtM4/Eqf1ubbGBZ6j88OfXM8v//Egv3vqCzVy7whj+XQ2bU6RyVQIBEyu3dFFo95kfDxPXtdJJEOMj+WUWKsthOdJXn7xFMWC6rY3bZeurgiFnEMo4iOeCBCN+SmXbBYWKrSlQqTbQuRydQxDIxIJYhg6E+MFpqeLdHVFufPuDbz/7nk+PqDs4TRN4A+Y7Hl8hJdfPAkotwGjtcG4sAKG4P51Rs9+XQkkB6NwZ9f6VJiOzjCWZXD61CKP7hnmmd8f4f4HNxIMWTSbDrW6SyCg4/eblIsNXE8SCvlYWChTKikP1FgigK4Jnv3DMebnyivXu6HhOB7BgIkmBPPzZQY2JQlMFnn91TMc+myKvU9s5cnvX8fcnOqO1moOg0NJqlWbXLbG1GSJVCqE3XBJt4epVBpYfp0tI21YPoN8vsbifIVA0MLbfLvagK979/1pquGq2GRTEwQCa+f62brkfFHiT8CFVLOJbG3ZNN/xJJ731Tqy3/T6xoNT13Upl8uEw2F0/ZupivtzVtP1aDhq1KG15uvvz8BcVdIRhFcnJGFT2YCETdUxsz0lXMrU1Zh+R0pwd9d6O1DJ2QIcXlRRjxFTAdexEqQDanz8aL9YM/IRQmKHVfTfSy+cWNOxsW2V3KRpKkrv8Tuu4dOPJ7nx5l6koXF4UdnlDPhczpwpESjVqDlh4omg8iisNYlGLCbGC2y/vhvLZ1Au2RhGlUbdwXU9KqUGbosPli/aRH2qC7LkZepKlUX+2bxKMxqKC07lQROKtpCw4I6utSkm5abkeFbZqexoUzSJp85c2cIokfzytORnw39qcCr5h+OSv9l66fcNm4K7u9RI/uCC5J5u9drr2wTHs5JsXXJrx8p57w4JntwIf3985bu5nseLY5KEpcRgS1y93hAMtfhnVkBvpc+M8OLzJ6g3HHQBUghS7SEe2D3Cexm43qnSG/ejaZfudghQtIMLHk6ZmiRoKopC01PJT3Ef3Nap3qvQkBzKqJFie1Cw01fjd786uWyJlbcl0YDOI3tH6AmtPBA1AbOzZRKJIO+/c3wFvLUSmQJBk88+mWTvE1txWt3mSEjne98e4ZUXTq6x3LIsg71PbOPdD8ex8Dh7JkshX+O2OwZp7wyjGxo+S8c0NbLZGouLVXymjutKAhfY7sRifk6dsHFbEaKxeICzpzMc/HSKctnG79fZeVMv9erKa4rFBoapUa87NBoOXd1Rctnyshfn/FyZ554+RjRqkUwF2LgpRarNTyiUply2icR9y9Z0q2t1KMN12zv5239/K5MTBXLZKvF4gFQ6xDtvnmV2tkRbOsT9D25qKaQV/3R2VpnHB8OWypcHhofTyl6sPUx3T5RiocbMTIkuLUJne5BaQ2IYykbKbB0ju+4wl6sRiVokEkE6OsOt4AslRLvhxh4efHgzhYKi//T3x3nvnXNqvB/y0dEZXuNjetH3/BrUHseVBIImex5TVImP9yuh2+uvnOL2OwdIJEOUCnWKOUkyHcJuOirVynVJtYUwTIFlmbz95lnm5yrcdscAiwsVGg0lhDMMHdf12P3YCIcPzzA0lGSDgKmpElNTRZymy/vvj3LnXYPEE37lECCgVnOIRP10dIapVhzGx/OUiw1Eu6C9PYzeut5efekU99y/kSm7SCpt8i+B9xxPrhFEHc8p27iuoGRLqIHfv7ImJFqRwvtmlT6iq/VsKdcdCtUmupBc2xMmbBn4rnZNv1J948Gp4zhkMhn8fv9VcPpnKL1l9N70YHSxSiIUa4GQlcVgpqJScjJ11Rn16+qh3BVUfCuJEpGUmlBtSvINBUJdqZJ1ntgg1nT+ao7k43mVPX6h+v9kzqPbr9HeGWLX3mHKJZtSsc5P/kqJO8pluxUxGCEYNDh5fIHevhiFWBxTh45mlX/4xxP4pIeuQSS0gbdeO8vDu9SC77kenic58OE4ux8bZt/7Y5RLDQxDQ0qwLJ1du4c5dmR2Td5yPKHCAhxP8v605OYOwaFFSW9ImbWDIFv3eGtKKbrv6FIG6ceykqmy4nUq/qKyMAE4X5RfOnoXCLpCkiNZ1QX+U1W25TJ0qe7p6ro2CX9/HO7pVpuY7SnV5X7hvMSTHjemxSpPQsFgRPL3xz1ubofzRSg7ajPSERL81Yh61UdzqqMeMZV9WDRhEQyZ/PxvbmRutkSl3CDVFqa9O8Kpqk4HNkenC/h9Bu3hS/sfG5pKdTqeg4GIOtKFBny+oCgYTakSpJJ+xaMNGkqA1nCVSnhjDLoNl7dfOEFzlf+NLqBhu/zhmRP87V/vANTa5EkFKk1T4+ZbennphZNYlk4w5OPUCRvbdtn12EgrgUe9nwe0tQf53o+2X0R7MAI6j+3azIlj8ywuVonH/JiGRnaxyqHPp/EcSb3hcN/9Q5iGjq5reJ5E08QyLWIpZ71YbLToBBqRiEUmU8U0dTKLFTo6I9h1h0K+TiBoUq02iSf86JrG+GiOW2/rR9PA9JnYdnPZizOzWCXVFqS3N0YwqFMu25w5naWnJ4ZddxHLrg/K8ktDrAllGBvL03RcuntidHaG8TyYny/TP5jk2h1dJJNB5maLnDmT4Y47N3Dq5AI9vTH8lhJhSU+SzdWQSDZtThEM+dA08PlNkskA0pMUKw65bI2OzhChUJipySIvPX+Ces3BbiqHhEDAYM/jWwn4NV59+Qw33NhDvaE2qCPb0jhNj+eeOcrsTBm/32BP6xz+qS0OpZRYlk4qHeSxb28jn61RLtZ58vvXMTVZZOx8lk1bUgQCPiYnClQqiisbjVrk81V8PhPdEIyez1MuNZa72/VaE6FpWJZOW1uITz+eILNQYefOHpCo69FVEbNT4wXOn83x8YFxBjckSSQDbN7SRqlYp1LWaDQcursihDanQIBdd2k0HN556xw33tzH559N0tkVI9kWWj4+2bqk4bIMDv+Y+seTkr/eyrINYXsAjmbgtrRLqVTE5zOXwalAEDDU2rtQXxLrrvh5G5ogHjSvCpy+Rn3jwenV+vOWJgQjXSGOTJWpNhy+mCyyo2+tpLIrJLjQ1/zjOYlPFxiexJFqrJ+pK5/TWzqgO3TpXWnAEMtduAuraKtxTTJg0j8QZ2G+QqViU6s16euP47gu2UwNpGxxqyTTCzXiyTgDPpf//tsT+DwX13ExAyalYp3JiQK27bL3W9uolBpMT5eIxfyYps79D24kn1cCFE0ThEM+jh2bW+PVueRlKqUCPZvjgrhPdZFX7+qTfg1D87ixXfDetAShAM/D/Wt5UwLBdzfCK+MSXaiO6kBE8dAaruLjrl48H+wV/P1xNSKtOoKEX3kCdgTUu12qao7kV6clmlAennUXbmpXIrasSmxkogz94Uu+xfIn7o9Inj4nl10JjmZgR1rQcOGfT6kuxaP9yrz9/l4N25O8NamM/G9oh7Gi5Nen4bsb1Ybkwsx5D9B8Gom0n7YOxXN0PTUGv9YveWVUkrQudhZYr54YEnw2rxwLgoYgaCgxX1vgwmMlcFqtnqVz+NI4lAtlmra7fGzrrsT21MZMuCshAkJKGlWHZDLIO2+d5eZb+pY3VEud03DER0dHGMuvr/HC9ACzRXsYEAk8CdWmx+EFiOfL7N8/QThocurEPEi4+dZ+ZqdLmKZGOGJxYP84D+/awocfjFIuN6hWm0RjfuyGw8O7htm/b2xZqPTIrmEOHZxi+/XdjI/lkK0giXDEAgGu41FruoTDFrouqFSUY0As7iccMRFYzMyoe2bT5hSeJ6lWmxSLLp4nSbeFyCyWKRYN+jfEcaVy9HhzUpIKQHupRK6qIjozJZt+0+C3vz5MJOInFFI+pbVqk/m5Mvc+sBHTp3PmVIY77hhk/wdjpDvC3HhjD4cOTjM5WaBvIM4ddw5w5tQig0NJajWHctmmqztCJGwxP1eiVFAm8nbD44Vnj2PbLp5Utk2ghEIvPHuM7/1wB719cWZmSvT0RhkeSZPLVclmq/QPJLjm2i5lZN8eIGA0aPAV5OGtKjQ8xspqE5tcx7pLMwSBgElnp04gYFAq2UxPl+jtixKN+alUbGy7yebhJI2Gy/RUkVq1STIVIJ+v4TN1fvzT61lYUElwhqGxcVMbUnr8/jdHuO3OAc6cyZBMqSjnfKZOLOrHZxnUqs1l+lSj4XL40AxCKBrTTbf2YRoapqmzuFhlfqGCP2Bg6hrVWpN77t3Iwc8m6eyMkk4H0ZD4TY3xkuDzBZftqT8eyH+2oK6h1dGiHUF1L0Z8gswlfi9hwfSqYL1cRbkeND2J60m0LzH4v1oX11VwerX+7NUW9hH0aVRtbzkf+HLlSbVgnMjJFpBSfMub2hV39I+tqE/yRc5gOOAQCJuEoxZCSmrVJtWaTThscfjzaUbPZ6k4yjdzMCI4c6aEsB0KhTqelASDPrKZKq7rMT9X5p23zrJzZzeTk3lOnbQpFhuEgj4CIR+bNycZGEzy9ptnlu16YMXL1PDrvDXpoWuCu7oUeKw4kpfHlbAkbin/0vaAoNqExzeIy6ZshU3B3kElOkpZgukKfDKvLIwcTwHGa1IQNCBXl/g0OJKBe3qUoOxIRvJBA3y68ghcr/t5JKM4v6amuthBE+Zr0HDgri7FhbvSMdwdnYIXx1ZeHDIVwL29U3UVgxdYS/k0waP9goMLHvtm4JF+dVxeOC95dODi1y+VlMrTc3XpmuDhPsH7Z1SS0+bU5T+rhroWLwfcG646dyn/kok/nCuqDVchV8OV6nuB6vBGfUsbJxVXu0EkmJ+r8d7b57jx5l4GBpPs/3Cc2+8YwGfV8QcMQmEf7e1hpPTwVqu91nzfFYFdwBD0RQRjp8uUC3UK2eryryxxr11PXYOLi1X27xvjnvs3gpRMTRaJxf309cdZXCgzuCHB3fduaBnyW3R2RZmfr7BxY4qRre1kMhXlm7nUfa07HP58mgcf2cKBD8d4642z/OAn26nVHBzHpqcnSqPhMDdbxnU9Eokgll+jWnU5sH+UDUNJJsYLdPdEEaYg6lPXbsgQVIo1RdMAGlUVB6zsqGxmZ4prDsvBT6fYtWeY3XtHOLB/jN2PjfDULw+xuFDh7vuG2Lqtg2y2Srlkc811XczNKgHjpk0p6nWHyYk8lmWwcXMbtu0wOVGgXndwHDUxMU1NddmkpFZzmZosMLKtnXLZJpupUCrWiceDNG1JIGASiVrK/7heg9wcpIYvf/Gps0q2DpkGjJckGioc491pSU9YsiUmiK3ipy8l4zVqLoG6RsAfwB8wcZoen3w0QXd3lI7OKEcPz3Ls6ALJZADXlYyN53n40c28/OIpZqaLKmSkN87hQzNs3wGffTLJ7XcNsn/fGMGAj4ce3sz581kOHZzijrsH2b13hBdargtL9KlXXzqJ5TeYn6/w0f5x7rhzAF3TlF+tZZBuC9JoeBSKdcbH81xzXRfBoMGJ4/N89sk0ux8boVRqsMXUSMgoOusnZ6n7Sra8sde/TyuO5NCi5K8voB6dyEFP+PLPmaXlRUrJWKbGVK6Ghkdvwo+pXx3nf526Ck6v1v+QClk6VdvDd4kbt+FKPpxVcZaZuqQrqEyUl8byJVvFK2YbqsMFAg/Z4o5dOWA1NSjZyoB9oi7pN2rEokk+eG+MphTk8jXm5tSWWAABv85gb4SXJyT9xRqlFjAFqFRtYnHV6fCkpJCrY5gqwajRUCbhmi4oFmqcPLFApdrkRz/bycJ8ec2o1QrofJ7x6AwKtq4arQcMeKRPsFCHoxlFxL+vW/DKhCThFyS+RCDl18UFo/olGxrJ4UX49WlJOqA8Qjtb3YL3ZyV+TXVOH+gV/OGc4o4Ox2FLXI2ysnVYrKsH5I42wQ1pwVuTHhUHHuz9egtzwBDUHEnNUbzNwSg8dVpya6fgmuSl33NnWqMzqIDgX7V4s384K7mv58sfMKtLCMG2pCBxGVuspco1FCfYkerBb2lKdDcUW0sj2ZESzNWU4Cvll2TqSqSVTwY4t+qjCaHSspZAfjwRoN4aVXd0RZiaLHD0yBy33t7PubPKVLWjXUXk/u6pw1y7vZNb7uxfV0gDqvtebioQnGkI4jELKeUa0LYEHl575RSelMRjfhbmK7zwzDG+96PrGNmWZnG+zPlzWYIhkw0bU4yezTC8tZ1//ofPljtFpWID09R57Ftbkcg13deFhQrHjs5w/0ObcByP0fM5ursjOI7OwmIF09BItQXx+Qw0AWfPLPL8syd48OHNfLR/nB3XdzM3W6KrP4aUKpRiJCGYKAfwr+pUfXFomj2Pb+X3Tx0hGvNTLKhEQL/f4Iabevlo/zgPPLxJKe0bTf7t393C7GyJbIuOcMPNPYyP5cjnqxiGTq3WZH6+TE9PjLZ0kEbNYWKigBRqIwGKi+7z6XiusmBbGj+XSzaWX8fzTMKRBKVCnWq1SSxuEXZMXFfiOJKFRZtY8U38t/ausUjK1iVL5Jimp9auTxckA2GIWYJb2gURH4BgKAZnCvDOjERDGfVviLZM9lGd9H6/RjM7Tk2myZU80gkTKaFSaZDuiLJNaFQqNtGonxtv6aVWa/LonmEK+TrlcoNI1OKa7R3YDZcHHtrM6PksO67vYmBDkpmpAtJTnryWZXLm1CSP7t5CpcWRllLys39zI1MTeWzbo7NT8fU9z8PfNJSl20IVyzKJxSy6uiw0TTA3VyaXqbMwX+bX//w5ex7fyuuvnMbzJE9+7zo6ukMXAVSJ5DdnFPBcWj1C1Kis6kwvWemu7pq6UlHCfrjp8mvHm5OSezpdDo5XqNQdtBaVKuq/CrG+bn3jj5ymaQQCgcuKHq7Wn648KTk2XWax3ESiEbLU2CvfUHnwS51QSxfc0qH+bIch1jxsJJLPFhRYq7eEUgMRyaFFZby+q58vBWpLtS0Jr01IbmkXjBYEh7Ia96dg+/YuXn7hBJWGQ6CVJBXy6zzxuDKlTlgQCfuWgSmoB/FSx8m2XXRd8MlHE+zaM8zLL5zEbrrLnD1D17j9jgECIWPNqFVKSdOTzFTg3u61n9WvK7DSExL0hASnC5J3Z5QY51Sei0bXV1q6EOxMq2NxeFEyLWFbUlBzV8yoR4vKsP+hXgiZgmfOK86vJpR358aYYM+AokgA9IYF7898tTFbxZHsn5U82Kuxb0ayZONZbkr2zUieHBJrHhyXqq6Q6hJrmjKuHy1JXp2QPDGoQheupITQ8Pks9CuwknI95U8abblMZOvKAPzFUcm2pDICf/a8ZGtSeSxqQhA1leDrV6c9vt8f4Quf0RIrqc7X0kAhHNCJt0eYmVYG5kiIRK0VsVDMIhjy8WmtSbmszHQikctzEaoOfDyvju+ufkmZyPLIdamW1fK7thAImkxNqC5lKOxjbDSH5TMYGExSrtQp5Ou4jsdNt/bx218dRjcE1UqTYNDE8ySNhsMLzx7n3/2vt/LGq2e49/6NSCmZnirSPxAnFvczMZZlZGsHhilwXJdQ0ETTBZWSjeOXlEo16nWX+x/cxEf7xymWGoQjFosLFXoGYrhSMBxX909HZwSfT18Wf+WyNY4emeXHP7+eyfE8dtNbphmUijVuvX2AbEZ1jY8dnaNUspGexPIbTE0WiMY2ItBYnK+iaYL2jgjp9iCu63HqZI5qpUFXT4xkIoCQ4DRVjKkQEqEJDKGmGp7rkUgF0DRBo24zN1fB8hsE/CbvvzPOuXNZnvz+dbiei88ycIqLkD0P7f9/9v4zyK77TPMEf/9zzvXepHdIeEeAJAh6761IkZJKKtel7e7p3tnZ7YnYD/tpN2Ijtjdm5sPGzMRubMRWz1RXd3VJJdOSKJGikURPgh4g4U0C6e31/h7z3w/vTQckQFBiqWaGeCIYBBJp7j335LnPed/H7KXdaYLyGTIS9luapqPoj8C3tqjL1G6KdGdrQm70ThU0vx6XG88buoSkVlUEMrswgGxKihEMDYsLdZoNSVLw+UxqNZt6Xc6vYNCka3sGy1T89pVznD+fp16Tf0ulQxy8ZZjnf3aMu+4Z5cyZJRzbZXq6xG13bOL5nx+jXreJxQKcPrlAu+3y1Df3EAn7OHJ4hussRSwexLIMatUmpmWQTIVxXcUH703Q2x/HNMUjoLXEmFUrLRQK01T8ww+P8Ff/8mYWtclwfJVoFpqwKbb6d79To/bxj/Af+A5tK8KvLngMxxTCJTUNB8o2fLyguS4j8iHXlWuCUuu5wuc5zUjEY3qhhEKyTdMRH71xP9m1NYbX8KXwtSenPp+Pvr6LFY7X8I8B2/U4PFGm1vbQGIQDFpu7ZSoQ98PPTmr+bMdqZemyfu9iKNS6Bg/Hk7ihuF/0Qa9OaJ4YZZ3B6HLwGYq7++H9ec1Q1CToT/Gjc/CdraENDSSBkEnF0XhakUgEL8ma/PB9IaOvvnwGw5S7/A8OTfDokzuxLMX8bIVoNMDoljSpbAi3Uxm0dsj1i/OakZiYv9ZiICIB7Dd0yd+3JeTfL5Q1cb/maE6zN7PxTZaHGMfqjriZ+yKXTpgDppioXpnwOFNaa56SyeVr05qAJTq2K7nuAd6e1WxLXh0RXMbL45qgKTcqYQvu7Jc3hVcnJQ4qcZU3HCDGOJAp5ENDik8WNS9MaL67lTVmqlXM1TXvzAoxvLVX4TMMsqnUVf2si7Wl6aAiHYRdaTg0J878pzapda+n68E7s5psCOIRkXK89MJJmm0HjUytAwGTZ5/eycdlk3RHk5zP19m3v49AUKpCy6UW5XIL01AoQxEIWvT0xq7o8o77RbYAcqNnhPzi3v7FiXXncrnURAF9/TEs0+CDQ5PccGCARCJIbqnO5GSBLVszTE2UyOcLEgVUbODYHql0aIUsg5CIC+fzPPbETv7ubz+htz/GLbcOY9supqHYvCXL2FgegJGRFH39UQzDFPJRbWGaBm+8Pka51CIYsnj0sR0cOzrHjTcNXCIVCYRWj+cyoZ+aKJFbqvPYE7tYWKhQLDbRGizL5O03zrFnXz+fHZnhuv1yR1gqNAhH/GSzoptMZ8KYpkGt2sL1XNptj1KxTjTiJx73MztdZnG+yvBIilg8QL1u0265GIbC55OWp3DETywa4N/9fz8gFhdy3GjY5HN1DKVIJUNUSk2sdIhkMkS1XKT46l8ze9+/5ZNSmDv7VOd36stLmUKW4vouxV5Pcyyn+dk52JKQTccyliUfLpDtClGrOuRz0lwXTwTp7pFrtW17NBs2raZHd2+0U16ymhbx5utjVMpN+vrj/OJnxwgGLPw+E5Tmu392A5MT0gIWTwRJpUJUqy3efGOM8+fyLC7WePzJXSKPcjRRv8nEeIEPDk1w3wNbeeO1czz19B4qldbK4y4WGyuGPKftMjddwuvL8vyYSzoIlpKGrfsG1hy3/HmKb/17ukcPQvceFGA7mvsGFD8+q0l34rdv6lYr1xLTNElddE2otDXjFc0jQ4pDZ+U3qifuZ1ffFwrrr+EL8LUnp7LO0iil1vVOX8NXj9PzNWptDw+DnX0xupfrEtHM1uEyMrkvhGUo9qQVu9Oa8TLMVjW/Oq/pj8gqKxW4cuxKoSW6yI/mNfcNQtuRXNGesHnJVNMDPE/MM67yZCq6JmtycbHGp59O88/++U2MXyiQz9VJJIJgKMbO5bDbHrmlOruv6xFiehGO5+UNYqMO590ZaWEyc6ttSdsSio8XNHf1wY/PwYWKR28Idnf+XSFvTH97QhPzSxas68F7c/DQECvTvrV4ePhSgltzJJ6r0BL36hdhNCZFCaC5c8PYr0uxNyMmt5+NyXF5dFiSBqaqq0T898WNXWIqe3Fcc1ufVOGCrLhPFsVw9yfbpL5wri4ZB+mAuNJ/HzIAQv5v69V8tqR4ZVITskSCcqIg/z4ah6UGOFqT6ZabocmZCktLDcKJEEMDMVIxk7fHNFvSctBr1TatlrMSfdZqOit1o4GgxWOPX73Lu+4IId8UM+jpjYp+r9yiVJIa0HDETyBk8dtXznDHnZvYtaebUqmBZSqGRpKEgj7GxnKMbk4zMJRgerJEKOTDF5fVdzwWwA75aLcdTMOgUbeZmCjwl98/QC5XZ3amTHeHSJ87s8SOnV3MzJT59KMpiqUm8ViQ2dkyd96zmUK+zo4d3SRTQaJRP8eOzkvYf8c4uBYerBzP5ZvLVCpEMhXmzdfHKFda+CyTcrGB62qeeHqXTJtVP4Vcg2DQZHhTEtOU7NWxczmSqRD9/TESyQSe59FuOzRbLsVClVg8INpXwPIZ3P/QVl58/gQ+v4lhAFoLoX5iJ41GG2UoGnVb4uQ8LfFcnqz+Y/EgPr+Jz2+i7RLG3BkOWhfYt3PPV/L+ZBmK/V2KqqNxPJme+zaYui43xPUnEliGwtWapu3gaQh2pEqWT5FOh3n3rXFMS3H6pOjqQyGL7/7ZDRw5PI3naiIRH5lMmInxEjfdOkBXd5SpySJ+v0mgL04h32BkJMXBW4ZJJkNMjOdJJEJksyEunM/juh6PPr6TD96f4P4Ht1FvtJmcLKw81mQyxJkzS7iuh2Eq8rk6t+9VjMYULQ+KLcV1Gb2Sn+13apTf+GtA2qV44t/ScsNsTSp+M6V5YEhdJkNar2n0lX+fqcNQVOEzDYJ+k1bbu8wU+xq+LL725LTdbjM9Pc3AwACBwO9TV3gNV4tay0Vj0JsIriGmAIqPFzyCpkzvfl+ozoRvIArnSrJaPV3QnK/Kqn+jC875sub9ec3DQwqfllixgJHmaN5HT1itM5CAyBKO5jTVNiR6w3z8wTSPPbGTaqW1xjEtBqq9+/qYmS6xsFBlJt8Sw0ZQ3qQCIZONrGCHlzQBAzIbEEC/oXhsWAjWnvRqhenejOKDBZEBHJrTTGsotaXYwNXLOjV4bEStTJMvlDUvT2g2xeR4xXxcsfzgw3khjluvkiTeO2hwS8e9PxzTDF+F3nN7Ukjsbb2S7drdSQf41hbJv920QQzWdE3jejC8AZm/GJsTioGoyDi8zuvqM0SC8O2tSib1UQnJt22bmfk8vV0ZLOvLrOYk/grkXDk0J3rcnrBoPJWC72xVfLwogfzIU1zRAFYSSQZ6U5wqePhdSCA3RUYqim0Y+DyPjz+a4sYDAyvnXbHUJJMJk0gE6eqOYPmNDc8tEA1dq1NO8faM5sYuKT/I+UIMDhlcGMuBChKK+DBNxaF3xrnx4BAT4wVGNmWYGM/j81nEYkHKpQZKKxoNh67usESvVVoopYhG/bgdQ5DP58dxPGLxAOGwH3/AJBrzEQwnqZZbzE6VyWSjFEtNEskw5VKLtGkSDJk8uH87Cs27R+dRBiwuVqnV2vh95hV/jzZKJ1Ba8+Aj21iYr3YamRSBgMVLL54kEvFzx12biI0kqFTaTFwoUiw22bw1zR13j3JhLM/JE0tEIn4SySAz0yUOHBxkoD8uRQ5KJs3lcptMNsSf/bMbmZkuk8/XyWYjJJIhCrka3b1xYjE/rZaLbbt4rqRsWJ1M2Uw2TLNpo7XGnxnBmTuzYT3nH4pbeuD586JZvXfw8p+3UUOc7Xm02i7hSIiTx6f4kz/fz+x0mVxOGpz6+uJ8+P4EJ08uEgiYPPH0HkpFmcDWbYhnAtx51zCTkxXOnlmktzdGtjuCYShs22FkU5pKucnsTHmlNKHeaHP3PZs5/Ok0N940xOu/HQMkxiwS9VMuNonFg9htl0w2jOsJEbcMiFisNK95Gsifpzl+GIDGhU8J5i8QsHbz2rTm/sHLa/eXYyczmdVrgqVE8gXQFQswmXPJ1+wNv/4avhy+9uT0Gv54kFWjIraBSPzBIcU/nNEUWx7JwMaTtvNliSmK+VfrSC0lBMxQImifrIoW0lKyGk0GYJspMUojUc3ta6Z4uabm9WnRMiYCCsdR5Fvi+r3rouipmiOE9HgeZuuwJw0fFQ1uvX2El144ic9vrtT22W2XR5/YSTob5L4HtzI/V2Ex38CKhIhnowST1mXJg6fB1pef1SmlsD297t93JkUKcHe/wWc5TU9IcbaseXxYnle5LWutyJrDvimuGI7B8QJMVIXE789KreZgdLWdaRn3DkBXSPGrC5rrMrA5ri4Jq74YIUsRtTS/ndQ8OrycQSiT0IHolY1r3WtW5T85JzWeF+NCWfPGjOa5LV9MTJfNcj4TesKK8bLmqVG17mZII5W4F8qaRksTtzW9XV/4rWm60hI1HIXPcnBnH7Q9zetTmt6IWnfOLWOxoekJSQrBsg6u7Wk+XdL8s51i2vh0Saonb+xSYFo89sROXnrxJIV8g08+nmbP3l4iUSFLsZifdDZyWbK2DFdLraXPkJuaI0tSGXtdl6Kw5DE0kmZhvkKx2CCbjfDoEzulLarcpN222bq9i6VcjfPncnT1RNm5q5uJiTyNhkNvX5xEKojnCtlqNmwcxyPSyYkNBn386hfH6R+Ic9e9m2k122gUqVSQWDzI/FwFx2kxOJwEJP2iUmkRjfp5/Bs7mZ2pUizUyXRF6O6JfuFzhfXpBBohrP0DcdLpELMzFZ7/2XFaLYdEMsi7b41z0y2DpDMSLRaO+PD7DNpNh57eGLFYgGKxgVKwa3c3pWITIxUin69hmRKBFAwZtNsupVKTvv4Y2a4wM9NlYjrA1h1duK7LQ49u58VfngTAHzDxPI1pKh5+ZAeu69FoOOSWamwLymq4OX6YeEd7+lXB65wLN/0eOnXH8chkwiilGd3cxd/9zUds3paltydKOOzj1MkForEgt985wuhohrZtc+jQBE9/cy+fLHrM1TzuNU6SPvtrhu7619gqRKvhMj9XwbYdBoeTDA4lWJir4Lgih2jbLs///BgPPLydfL66Ymh77MmdHHp3vFMAYGD5TQYGE4S8GjVCGCDfe75KIVcnkwqS1g5WZpTy/BhtF9Tbf809T/1bwuHIl8ojHStrJiqamzvtdX3JIBO5Og3bw3a9ay79PxDXyOk1/FHQaLsrzVC+DZoywpbim6PSFuVqj6RfEfbB/syqRjAbhPGqxBO5ItWk5YBlCplbjpgqtDS2lgikmi3mqOGo5kwJIj6PbUnF2ZLm4wXJ9FyrZZyrw0DXepe142l+dEazKSbGgpYLrlaYxvr1YS7fwIiE2DIUJRqRSJOV6c2WFJW25idnPZ6IigP9YpzqVFn61qyNNsKWhGKqtpoXahmqk40pwfljZc2elOJ4QaaQcb9i3wZxSIZS7E3Ln/ek4NMlceW/NiUH99Y+tYYkKvakYXMcfnhG817HsBT1S7boX+xQG17YB2Pyerw0ISt+gExQ2p2u5n1Aa00yAMMbNAwnA0LmJysay4Qt8Y3lG57W/P1p0bH6TcnI7YvIBDUZWP4cmKpptsYVT2xSuI5BLnd1b1RBU3JdX52QaXXL1RRaEp91cdSZ7rSYOR40XDk/l3G+DCNRienKhDTzdfmK5YmxioR55Jv78MpVyoUGgaDJps0pgiETZ43s5EooNOX8OVsU0939gx2zhwelYgvTVESi/k4WqKy/l3Ms67UW1UqFTFeYgf44S4s1PvlkmkQySKvpYhiaZ569jldfOkWxKMG2obAPn8/gsSd24XoeN986TDTmR6Ho6YsSCprYtqZUahKL+Yknw7RbNoWCGKC6uiP4fYpW22VkNEkmEyKRCeF4Ho6WWskvk86hlGJqqigGGstYqWqtVtsMDSdIJEK8/ttzuJ6HQgL7s9kwjz+1i3QmRK3Wptm0icWDxBNBKtUmvb0JFhcqzM5WiET9pFMhwmE/gaCFYSiy3RG0B7Vqi1on2P+571xHfqlOsdggngySSodpNW0KuQYffTTJwZuHaM8cX3ncX3Z6qtHkGtD05BzLBNZro/2GbAt+X0SjFoVii2jUx5//1U0szFcpl5tSRTqawvM00WiAfKHOm6+N8cBD21AG3NKj8NkNyj/7dzTHD1M5/gKhXfeTuOe/Jrm7G9t2mJ2pUK206OmLk8/VOX1ykWg8wDPPXYdlaQ5/kudbf7IP19O89/YFlhZrxBNBPE/zJ9/bTyKiqb3/QwIHv8vcEvz6Vyeo1do4jlwnQpbDw/f+X1Bv/7eoxTFK5z6lq3CBorWnk/xyKS6UNadyHnZFY9Q8TMvDUPL7s7zB0np1aPCPUf/8dcM1cnoNfxRMFporf75cvEYyaPDkJplEldowXxPTyEJDajqvy66PQtLolfXNWuzYsOdckQ16fLoER3KabAC2Jdevg+froikKXGTQbnRife4bVNQdiVyaqWl2JNX69eGWFJ8ueLwwqxmOeVRtIda5hoenodSWaW6+JQRtPcSR/s3Nis/z8vfLEdSpqqZrzRtN3ZHpkAErzVrpgOZkAeZqmj0ZxfYvMCfF/GIMA0j4ZWqXa0DMt55IhyzF93cJmVuun52ucVmieVuvaDYXGxIsP/AlG1yUEod7pQ0XG1+TAcXTo7Ly35YQk9rF7wnltkxDh2OrRQy6kwuZDMBSU8gqWiaqBjBdBe1qCnXIfEHGaedRsj0p59OFsibhVx1DRUeXVtOMlcXoZXuid31ik+K9WU3AlCiqVECigZZD//2GYmtS5BQHO9MtDTg+k4Vwgs19SdIBuSGzv0SHY8uVR/XQRW1pnueR7Yrw858c5Znn9kg9aCzA0lIN01RksmE8z6XeaJBbkj70WrVNb2+MY5/PsbBQxXE8nn5uD08/t5exMzkKhQZdXRGGN6XILdUo5Bts35klmQxTrTUZP1+gWm3R3x9ncCiJaUqPfCLhp7s7QqPp4nnifA8GFY7jYpgGrbpDLBHgVMHFh0tv+KqfPj4FuaUGZ08tctOtQ5imQTBkdW6bpD3p/oe2MjdboVJpceDgENmuCLVaCzT098collpMT5WIRvz09sU5fnyOwcEEIyMpymWJSIpE/RTyDSIRP5GwrPGbLZdkPEAg6GNpqU7bdklnw3R1RQiGTCol+Nv/6WOy2TA9PRHcan7lcX+Z6Wnb1bw7JykXmaDCVJJ/umyC+ypgA9lslHqtRaPpEIn4iET8BEMWyWSIWr3FqZOLRKJ+nn52Lz6/QmuRSa1dqwM0TvwOe/ECgef+P1Qb0Nsbw3E9Wk2HRDJAV3cv1YporUOhINff0E+uUMdAccfdo9QqbeKJAL19MZaWapw7UyEV3oxbc/nVL06xtFjD60SlKbdNBc3LbxT41v3/Fa1f/p/lQbz717Qe+rf8ZiHM5hiMJJZJp1yXHS03m9WwIpVWOAqUpzE7zv1Crc3ZhRoKTdhvXGuE+gpwjZxewx8F+aqN2ExW/BuXRdBUBEPrG4mmqx4fL0hMR8CEtifEIh2QyZjflO+rtZhM0kGZGIYt6TueqYp4/bktilcmZLLVv4YoHc17nFrSxIxLG4wCFpIpaOsV8rPYgB3J1c/RWpNraj5elNSATBB2p1RnRa+IWDLtbbvw3rym0paazrXO8QeHFMmA9MtfCVVbCOQyXhqXKKwTBTprWpkw394LHorxihhfntsiM6YraUtBBP5+Q0LgLybqgrUkUDFwFcOcrquMcBKs120uNjR39m389X5T9JPbU6txOjXbY6oGlbbiZEGmyRKSv/yIhfB+tKDJN7XoaDvD/JYrpq96G5Jfss1YoVYSDjwtK78LZZnWbE0oMkG1Yj6xPU3Zlje830xqmWJpWe0vP/ftCThVXP8zZIrq8dqUJmjJ119tbBpcWZsbjljc/9A2fv7TYzz97C4ikSCJRJBqtUU+L8Y+0zDJ5WoYBvQPJJieLpFMh9iyLUtff5wP3hvn/PkCDz2yjZ6+mEwUZ8oMb0qRzoSZnixJqH46zJatGZpNG9NU5HI1IhEf8UQIy1KdWlRNsyFXi2bDwfPgH/7+MPc/uJXi503CUT/dQwn8WCznAhRb8rs9GL3U6GMB87N1XMfl7Nkc5UqLe+7bTDQa6JBtm6XFGtlsmEw2TDwewO83UAoW56vYtkc0FmBkU5z+/hhTUyVOnpjvRFdZLC5WiUYDRGIBcos1QJNKh1mYr1Jv2PT1RkEZTIwXKRQbJJJBstkIpgULc3XefecC2a4wjz25i8WFMr0De2mcfJ1lI07pjb8mcdH01NWaMyVNuQV1W1FoayxDDJX3DKyWZXxx1cmXh2mCz29g+QJEwj4qZdE/u64mmQwwOBDHsAxc18XSFoGQibKrLPzur2k6ci1dlme1Fs7R/tF/SfpP/nscI0il0MJzPZKpEOVSi3bLRStNo2FiWYqB/jiTk0WGhhOcPLbAxx9Nc/0N/bzz5lncwjSJuI8DwRZLi1U83bmZxgMtFaqLi1UW3d2kYlncyhLticOM2BcIdO/htSnNkbzo0k0Fm2KK67vAcRRV5Kb8xFSZessh5DeJBS0Wyi0UHkHLYEfvV6cN/jrja09O/X4/IyMj13JO/5HRnwowtthAoZgvNRnOXH7c0XA0xwvLTnOZPBWbsFxxfkefrE38BlxuuuihqdswVlYczUkTUjYogfGuFl3pQFSiQOYaMqHqj1g8uLWn49Behd+QaaEGXprQ7M8qukLinB+MyjRxtq55aVyzOS6kuSuoNiSBIUvMCG/NaCI+tYbgKoauIn2k5Ur5wNpppgISAVioS5TW7X2Kd2fh87ym0tZ8f5fBsbwQ1Pm6TOJ2pbliZmhPWNHzJSZSXyV+Pqbpi2hu6THIN2XafKXmq90paae6rVdzPA/H8nIz4WnNYETMZWuJikbz0bzocAMmXKhohmPqIrOXD8/rvuRcuBp8sqg5WdSMxhS7M2qdfnb55789IyH8PlNupJYamsNLel1d6uc5zZ41mwKl5KZgNGHi4fH5kmRf3tK7mj7wZeBp0f96WshcwQYrG+Gp5/aSW6rRbHooBdGwn0hETE2pjJ9sd5hWy8Z1oK8vyshwApTB+bE8oYifu+4dJZON4A8YdPdEadRb1Gs2qXSQPdf1kMvVWZiv0t0dJR4PYpiKhAHaUzi2i2lakmHZ8FharBEM+QmFTJShmZ+rsrhQo1pp8eEHkzi2w7Pf3s/AQIQ2YHZuqn51XvPNNVpkE6hVHf7jv/+YBx/ZRiBgsTBf5ac/+px/9X+4jUPvjpPP1SWWy1T09MYY3Zxm67YMjWadtq2JhH10dUeZnhLNY19/XHTm81V8fpMtWzKYlmLsbIGFhQrpTJhG3SaTCdFtRmg0bGq1Fps2J9lspMnn6szNVclkwlSqTfbt7yfbFeHdt8/T1xej9/b/I+aW+1Zu1WwUdcfjtRmPpZaYGG1XNPgRC67LQia48fvYF+UDf7igma9r4n5FX5iV69rFWD4HQWHbLoaCxaU6Pp9Bd0+EVDrMxESB3FKN/v44gZCJoRS+oInjaXyFC7THP0Up+R1tOEKcTQX+xTGKP/6v8d30fSJDN5PJJEDJjUml2qJRt9EaTNPg7JlFQuEgv/zlSc6fzaO1ZsvWDJGgothuEE+mmJ+r4nl6da3jrsakaU+zmGvT3TWKW1kCRDrR+83/Jwe6wpwvr2Y8L197LMuiq6sblMjUFB7Ntkez7axkm+7qi1zTmn5F+NqTU6UUpvklRyTX8KUxnA5RrDvkaw6tNZWlDUev1FR6Wi5SHhAyZcJXsyVvc7B/Nf+01NIsVGXCVWiJ9mc5TcZQstoOd4LzHU+yPRWyxn1jWnNrr+LdOTGF/OTcqmv6gUHjCmRE4WnNXE2c/wrFYFTIns/UvDIhGXnDMfjbk5ojOVbW5BcjFVCkg5qraG4FxMBie5qBiBDG+JqpacsVnWPSL1PZsyU5TvcOyJv0q5NyYPakJW7r7VmPqapmugoPDl05YuvqIM9jrbSi7cn317DSSHM1sD3N5zl5XW/vlY81XEh8waS3KyRh2e/NysR4Sxyu71KU26IhPryk+WRRzqP+sDTqRH0Syj1ZlbV+vqWZq0sTmeoMUkfiIkm4WrQ9zcm8TLb/dNulX9twxKz36aIcs+uziokKDEbglxc09wzAR/MwVfUYjBrc0W8QtkRq0Kw7zM9VKOQbpDJhhnujdA+bHJr1OLKouX/wUnnLRjhR0LQ6AePFlqYnrPAZInWJ+YQ412yTO+JBwiFT1uqWQbXWplG3iXRMSktLNVLpEH19cVnHOx7dPRF6+6JEY4EVQ1Sz0cbnt0gkfVimaFszmQjVaotqrUU6HcJnGXhaU63btFoOSSU97MGQRSIRkj4kQ/Pf/j/eBCAa89Nui2konQ7zn3/8GX/1Lw4SiFrEfIr9WSnpEGguVBVpu83CbJlGw15pv3r5xVO4nsc7b41x931b+PEPjmCaCteTgoBCvi45ruNFtmzPYrddFuYqdPfEKBYbnDi+SDoTYsvWLLVai08/maa7O8rAYJxEMkCl3KLRsPH5gtTqbQwMGg2H40cX6eoOE0+EyGY1nqfJZKK88fo5piZKGIbBtp09nFsE3X0vu9J63e/pQ8PLz0w0joWWyJ/WXhe+LA52KxqO6FMnqhLndmuvGB+BFWNRoVAn6LdAKTGBBi3S6QCNhsepkzm01gwMxonGRINjtzVjY3l27MhgOFXKb/z1yrZow61M7jz1X/9fCe68F/XM/51S2aPRsAkETayYgWkqavU26XSUTz+dJr9Qhc7Kvlhs4tNy46DRxGKBjg5UYeChtbfuNzIW98Pi6t+XpRObuvfyWd7jaE6xL7v2K9TK+8PugRjHpsoru0AFbO8JXyOmXyG+9uTUtu2VeAif71qbwz8mumJ+8jWb2WKTkUwYv2UQshTPboFfj2t2p1RnbSl6xw8XNDVHdJPvz0PSL0YnS9FZkWqyncYdjcbz5Ca53YlQClnSOLUpDtU2vDunSQRktbnUhImyTFSLbbghq1Dao1gsE4vFN7xhabms65UPWmJMmqpq/myHaPhqjnzPcyW9onG8GCcKmsW6mJUuhteJ+lk7tegOwfvzsFDXmAYc6FqrN5XH9cMzmpu6hZD9zQmPm3uEjPoMzVJDr5ghbu9V/PCs5vYexd+f1nx3mxD6U0U4XdQkfHD3wMbmprXQiKb10JyYUiyl2RSHcyW5ydgUk9fx7VkImTJt/iLd6/E8nC1qDvYoujtGophP4qIEG399rqFJBcFnKh4ZlkKFvzslFat7M2qlDOB4TvPBvJBZgPkGbE3KsTbQYqZrAxqUdikVK5c9F5YxVxdjUaklNwlbErAjLKSz5YkmuulI0oRlimEvEYCTedkAvHBBmqN2JOV7dAXhbNkgY7jkF6pMN2xisSC/feU0ti1ZmLYt2ssnv7GL+3si2JbBBh7DDTEcg1wDRuKQWpOKETDhaE7Cxy1DEwlZnDg2x7btXYxfKBAOWWS7oliWge247NjZRbPpyCo75icSCZDJhDEM1WlHkyY0UCzM16jX28Qifrp6xdGdzgQBg2bTYXKigN9vkUwGCQYtlCG/x5al8PkN/ue//pD5uSog0UHdPTHGzubQWkoJMtkIU5Mldu7rwra9znkikorXpjXfGXQ5cWIBx5HMn5X2q8e202o62LaL67j8F//lrUxPSfRbOh0m2xXh7TfGyOfr9PTFiUZ9ZLrDxGI+AiETy2ewtFDDdTz6BxPsv74PhcLntwiGfKTSIWZnymjtJxoN0m45yKpfDFONeovnf3acQr5BpdIingjiuh5PPbOHE8fm6OmLkRxM8dKEx+MjG0wxO78PqQBcl5HzzHcFgup6ms9ycg5kgnKMVrXj8nsQsiAbgq1xzU/H5CYqE1QY5QaH3htn27Ysdcsm2xVhfrZMb784FV0HurrChCJ+7LZDsdACpZmbKZNMRUAZmJaP6E3PEj3wLMCKxGvDU1eB6VQJRmIEQxaO41EqNSiXpA3q8KfTFAoNHEeuP6aCZMLP7LEiAEvzRW64J4Lfb2Hb7srUdPlKEgha9HQFsQ+dX/djl41nt/WEeW9OryOnrutSqcj7QzriZ99wgvOLdSoNG/A4PV9n70D0mt70K8LXnpx6nke9Xr+k+eEavnp0xfycXaihPRhbrLGzTy5sCrXu4nu+LKvR+wYUyQA4ulMfasL7C5KBaSqJO4oHZGW3HFCtgD0Z1tWdtl3NeznNaBxu7JJLYdUWwro8cd2TAe15tFotolEP+a7rsbaxKt/SzFRlWrGl06Huac0b0zKV259df4Fqe5qpCoxXoGxrnhxddXmuxfvzcLKg+c42abhyPc3OlAS339OvyIY6RLxDxkMW/NUuxTuzot/61hbFT85pzpdlJTwcFdK5TE4NpWg5QiRtT9pQQpaQk+syikNzmp+NXTmeSaP5+1PSZf7MZtE8ltvSQHVzD2uMNvL/uiNxUq6n2ZXemEXN1mS6+ciwWqcFTgYUu1Iy3Uxe5KT10LwxBXMN+UldIXmTenhIMVGRG5rzZcnlGoyIpAPke2VCsDetmKlJrJPWMKlgRxr6wgrH0eRylz8XlpEOyuu12JRp/bky9IYg7JPXw2fK496VXo2tanuwNQ5H85qtSdEJP7tZ8cas4r4uTa1Q4T/9zQk8x+P+h7byox8cwbZd4vEA9ZqN3SFZP/6Hz3jiyV0kUyFC3aGr0hVGLEXkouSDfEuqH4fjmoil2Z0Cv2EwsinNyy+d4sCBQUzT4ML5PI26w/CmFOVKi2qpRTobIpkIYZgGuaUalUqb8fMFTNMgFPZJ1WipyTPP7cUXMJmZKjM3W2Xs3BKPP7WLQr5BOhMiFPSzuFhnbqZMOhsmnQrx0ounOX1qEbNz3IKdjOBw2GJiogjI769juxTydXyGiY2HRtMfhmN5zTe3msyfKzA9VWTP3tW7wZX613iAx57axcsvnubhx7aTSgcZGUkyN1/h3Jkldu/tRSl4/XfnCIUtnvzGbs6dy+N5mu7uKHv29XL402mOHJ7FdT3CYYvb7hglFLIoFhsYhoHjeCwuFFHGaquc9kSicM/9WymXmlTKTXx+k0DQB1ozPVVm5+5u+sOayAYxahdjo9KOi6EMqS79ZFEKIeq2bBg2KtWIBwye3aw5X4aodvnN8ye47bZh3n7jPPc9uBWAdtvDcyG3WKGrJ0G53KBcaaOUJh4PMnmhQDoTpVZtYxhQaVr4N98rub4a8k2PN2c0T49ufE2oLv/B0JgBg2QyxFuvnadQaHSekNzALJ8b0aCmXqmjlKJeqdOoVnj8yZ289KtjtLHWlVU8+sQOYhGF/6H/00UHCfyGR9QnxtKxsqbtygCjWHeolZps6wuzr8ckEfKxfyjOpxMlak2HfM3mg7ES/akAfYnAtSnqH4ivPTm9hj8eLEMxmg1zdqHOYrlFyG8y0tGetj0hE/N1TX9Y8dToqptYtrqayZpM0R4ZVpfkcF4ODUfz+oxmJKrYvUa/V2lrFhpCZI7mRVpwW/fVuJ41Sw2YrsnU0jKE+IBICBJ+xS09l65Y35qRKUQyoLh3cHX6evFjPVnQRHxS5fnIkOZH54Tk3NGnVgjmh/MyfY10Bv3dQSFcYRNifgOQ6aMcc5mK3t4xWY1X9IrRbFtSVrvjFXhms3zdmzNyI5BveET8Qqjen5e11WhcjGAnCprru9Q6PWTcry67VgxbisdHNP/hlJjKFhuaTAiuSyuqjhy316Y09wysJ6bLuPEyWaMfzgup/t42g4YjU7LPlkTLtj0pRPXuQZivw8mCHMe6A/GAvD41R7S3MZ/o6GrO5Qxgl0fTkRudu/sVqSDrHPAX47OcbARqjnR3J/2wJSM3YxG/YrdZZ3GqyYvPn6BWbxOPBSgWmrSaYiYs5BskEkHsqoun6dSXNvn8yCyPPbUTX+jqH3zL1eRbiojjkJ8pUV6s0dMVYWAoQdD0oRVkeiI88OA2xs7mmZutsHdfD5ZlMTFRoFpp0z8Qlwlgy2F6toJfGSwtVTlwcIC5uQpzMxVuv3OE7p4Y7ZZNvWbzxmtj3HBggNmZCosLVVCKt14/z+69vXx2ZIZ228U95XHrrSPs2tPNps0pCvkG8XiQaMxPd0+EXK4uE7SOnMdxJGnA9oS0f7YEAzFJ9vBZijNLNc6ezvHgw9sJBi2azTUVreUWr/3mLHfeM8pnR2bYvCXLG787z/0PbeHGmwaYniozN1fh7vs2098f5/ixOZLJMB99MEGx0CAU9nHfA1s5/vk8iws1XNfj+NEFtm7L8vhTOzEMxcJ8lWqlRbZHJs/ttkOp1OTjD6exHRfHFpOO52nuuHMTH380hd9v0tMbk3i84FcziTNQ9EUkb/hHZzxu6LpUD70WiYDiQLfi3OkirYZNudQkle7EabVsurtjHDs6x6ZNaX76o8947IkdhMJ+ZufKTE2U2Lazi6nxPMOjGarVNrVaG9s2OHM6RzoTpqc3ypaEySsTHrf1bqzRX4bWGtOnuOvezSu1tK6nMUzJl374ka0cefODNV+hOPLu59z96EEee3Qz1ZZBqdQmkZAIsEQmzDkdZNvmey75WVUgZMB3tskmyFDQH4brEorjjlwDlzOelVLcMJxgvtzi/GKdhuMxttigULPZPxT/A16ta7hGTq/hj4rBVJC5Uotqy2Wh3Fohp5aC69KwJ6UYr8CnixJjZHsSVQTiIl/bcnRlyF3/sbwEuK91iy80hJje0ScazoGoaN5sG4pf8F1PFTSH5mFXSvHPdir+rpOfCeK8v+Mip33D0fx6XBIGHhxSV9WA9Y1Nih+c0bw4DvcPqI72cRU398jPPzQvkVupgExO3pvXbE1I68+FsqQd2J7izj4h3UsNKR345uY1koGwYqK6ujbfnpBygt9OSxTNSExTdWAwovhoQUxc9w6oDaKwrgzTMPjeds0H85pyW4YYr09rmq6YLx4bUZ1V40a49OPvzQmR//4umU6ELLnRsIzVcoCJquSr3t4nx/7jRc3uNOxMb3xzEPs9VD2vT2tu61VXlUYwEIGuINha8dtJeR3enRXir5sun749xrYtaep18Z5LZ7lUbJqmkBfX9TAMhduJDysWm5iWwfxchaHNqSvWlk5UPIptxWxN0xs26GvV+I8/PEKzuRq2Ggz6+PZ399FuC4HLZKMcPjyLz1JMThTZvbeHWCxAIiFr6rbtUbUsmm2NaXlkMhGWlmrEY0Gi2wPUam0q5RbttsOH709w8JZhPvpggocf28FHH07RarncdHAQw4BbbhumkG9Qr9u4rsvQcJKlxRo+n0kk6qe7O0qz0eblF0+ve16RqI/+gXhnpS+ENdr5nVQepDMRHMfjw0MTfO/Pb+AHf/fpOoJaqbTo7omQTAbJ5ercctsw83NVXNejtz9O/0CcfK7GT3/0OQ89ug3taR54aBvlcpNqtU0gaPLcd/axtFhjaalGLBaguydK23ZZWqgRDJps39FFtdbmzOklAkGL3t4Yd94zSqXcYnqqTDwRIB4PcuzoHLVq+4rtV38IluU4psGK3OVKcNoeExcKhMI+SsWGtGr5TQqFJqOjPtLpCEuLFZ799nVMT5Wojpfo6omwe28Px47OMjiUIhS0WJyv0Wo6dPVE+OzwLLVam1BnEr6zN8K0bZIIyiaz4ch1TWvJI15J7mB9rvT4fINkKsSmnhCvPX+IwkJxZYPmD/o4eKAH993/F73bH6IU7iEezxKK+MlkI9g+k1NTHlsSl5cwRaz1+dCOoxiKKQqm4pcXNCMxMdlmQpCMBrg5FuDodJly3aZYdzb8ntdw9bhGTq/hj45owKTa8gj7Vyc9R3PS7hS2pGYybInmtOHA7hSXXQdvhKqt+Whe3P33D0otJcj69tC8TD5v7YGtiVW9laHUFwYnl1pSiZoKwM09ivmGpi8sGseHhzf6Wqnv7AmvEqeN0HAkPF1C16We9ECXYl8WNtZZKqZqYsb67jbVmQKKxKBiw44U/OqCkPKgBQ1HvkejQwTjKxMKze+m1pOZm3vg5+dlGjxelaimx/vhZFHW0YOB5enixs9FI8eoJ3Sp2SpoKs6VNDdk4WgentkssoLjeTZsgLocPstppquavogY3NJBzaT4Inhi0+pxHo4q/mKHrNrfnu2Q9gqcPa9pOvK6NFyZLptKUe9MNB8ZVlzNsGqxoQmaVx+Ttazze/685o4+xW+nNNmgIuSD+bky4aBJsdCQ7aOGeq1NIhECtSo/cV0tJk4lcoVkMsjiYpViocGISq2r2l2LYkvz8SIc6JIbG6/h8D/97Xpiipag+B/+p8P85V8d4OcvnuaOg/3ccecIL794imbTZmwsTzweIJUOc8utw5TLDfLhGOmuKB+/cZadO7tZmK9w400DuI5Ho6EoVZsEAhYPPLyddsvhG9/cQyHfoK8vTjjsw7IMDFPxwaEJ8rkGqXSInt5BFhdrNOptenqjBII+PNfj1y+cZmG+umJMCYV9PPXMXqKR1TipnrCctwZQKrVIpUMEgxbvH5oE4F/861uYnSmTW6qTyYYZHErQbNrk83WKhQY+n0EiGcTvN4mE/fz4h0e4/c5NHLx5kB//4DP239jHTTcNYRgQCpokEkEc28M0Fd3dUlUaCvrwtEcmG2Zpqc7hT2dJpAJ0dUf5+INJfvvKWRzH5Zln97BjZxcemvxSjRtvGqCnN/YHEdN8S3OhjDSLrcF0TXM8p4kFFN8Y3ficrbRF158OyFRwfq4sZQOlJsnd3Zw9l+O2O0do2x7Hjs4xujlNsaDI5+v09sXQfZr8UoP5+Srbt3ejTAgEDCJRP82mg2EqwmE/C/NVSsUmL/zyOPc/uI1w2E/bDOBicLRiYDuiI+8Jsy67eG2udO+mFFVbY+bPcPd+yG/fTqnYJpH0kwnXaX3436MK57HPv0UslmXzn/x3tFJb0VrjQ7wHr09rbupee02UqWjDXb5ZXX+cam2XWKTN3nCbCwWXRcNC+YMMRQ22JaFUtzuKZ5jINxhOb6CZuIarwteenJqmSSaTuebY/yOi2nLRrK8x3ZmGfVkhoC1XtJs7kootV+xyl3Dzuc5kNdcJM2+5Yq7qW7MibjgSu7M1AU9u2nhyZhgGsVh8JVas5Yoh60xRpq1LDbi1V3H3gLQJvT8nEUSjG7QXCRRdIWmW2oiYuh2N6mxNdInZkOLJTYq3ZjSbE/L1l8OulOLDBTE0gJC0aieXNGgqmo64z7uCEjMFMrl7Z3b947ulBz5aXGU0lqFIByQr8ZYexbG85pVJmRDckFUUWhL1dKALBmOyEm+7mrc7jVGelpSA72zb+LF/f5f4WyM+kTr81S7F76Y0Pz7rcd+AIrsm23Ytym3NqYK8Do4nF/+dSXEYH5qXvM8tiUt/nmWI2Wg0Dm9Oi3nikWHJfv08J8dkb0YRMIVM2x4ETIXnrT8XLsbpoubwonRxfzkontgEkxWRH8w3NDG/QWG2QaNu098XX9HGlcstwhEfAb+1YuYxTIV2hJgGghbRWIBatU0yFVo5FzZCrima5XRQYRgGZyfzNC4ipq4r6+VG3WZhocqWkTgz02UmLhR45PEdlEtNSqUmiUSQcMTPJx9PsWV7F/ZQnM0Zi1tvG+HQOxfYf0M/S4s1AgEfhuo0TnU60A0la3jTVDJdbEmUhudKbmo47GdoOEk47OPzIzNUKm3S6TCbNqd5580L3HjTADt3dVOtioFo+44usik/zTXPdfn33rU1UxNFjh+d5zvf28+PfnCE9w9N8v6hSbZtzzI0nKC3N8Ybr59jcb7G/uv7OT+W58zpJep1mz/53n7ats1Dj2zn1ZdPc9sdI3znT/dTyNc5eXKRnr4oPX1x2i2XXK4hUoyoH+2JvnNpvk610iaRDNA/0E2r6bK4UKW7J0b/QAKf3+SN18Z48OHtDA0lGNqUQJKPpH1umZ1apoTXO65emYw3Xd2Jkbr0/EsH4HdlvUJONZrPl2C8qnmw0wh2OZRt+GBO88wWuWGbn68RDvtotVxCYT+thkOjbhMOWxgKxs8X2LGrh6WlKktLdVKpEH39MSqVFj6/SSRqUSq2qFSaZLsjNOpiogtH/BQLDRbma7RaLr995RhPP7cXrTW7ghaJrgDuFX6LtNZYCrqNGvOv/b9pT3xKJtlFdziJO16kkFskZLHCbN3KEsVX/8d1LVv7MmJo/XBB03IkvaVqi5k25pdIwLv6pfwjHRDvwlgR/LUahmESwkO7NiG3zVAkxrn5BgpQnRcu6LumOf1D8LUnp5ZlkUhs8K52Df8oqLddqi0XMMjGVh0ulbZMPKu29LzftkH14zLanuZcEc6UJF5pe1I+ngktk871X1dpSwf7g0Nq3R3yxTAMk3B4NdxztgavdUjyzqRiZHhVU9gVFO1rdAPt6+vTmnJbQqYXG+vboNqe5tCcEN+pqsQJ3d0vRCXfgrot5pzZGviM1erKi/H+vCQVLMey3D+oOFWQqeu3t8JDQ4qXJzV/sWOViL83J8UAa9Fyoe+iPNOdKZkoKAVPblp/gR2OSQTTeEV0sXG/tKdsjqsVvey3t25M/gXyL9uSmpotn/fAoOKVCY9fXtA8MKguqSp9bcqj4UoE18090tLVF5bnXmxJgP1Gr8Na+A3Fg0OKEwVZ9T+xScjsREWyax8bkQn7sub04nNhGYWW5sN5abt6evPVyTQuxomCNPbUbNiThvMVzbaOli8aCxAIWLRashZcjj36zStnaDRszM5K3x+yeOTRHRw7OreiT7zSSn/dTZ6CpSW5o9O6U3nQieNZRj5XJxjw4YsYzMxM8JuXT+PzmYTCPs6eXaLS0Xxef+MA/kYJ1YqQ7Q7x4KPbmZku49gu8aQYpRoth0bDweeTg1sqNvnwgwkCAR97rutBoVhcqBKO+Ni2Pcu7b5/n0LsT3HrbMG++Mca5MzmmpkrcetswtZpNKGQyMNhFJBqg0WjTzgTBWz9nVEpRqTRZWqxx8qTkBf3FXx1gcbFGPlcnnQnT1R2lVmsxNVEmt1TjxpsGyS3VVya57759njvuHiUUsXj22/uYnioxM12mbyDG1m0RLJ/Ctj1+/cJJmk0H1/WoVdu02y4PP7YDz3PZvaeXcqVFbqGKRjM4FCcYtlhaqGFZBo8+sZNQyGJhoUZXd5hm1WZ6qkQx3yDbFSGeCDI1WSQY9Al5j1hUPMUbM5LOEdtA5+1pVmLqlm/000HFEyNfnMIxEFHEA5rTRTHxxeIB3n9nnEce38HHH07x6BM7efetC9x+1yYCQYtUJsz4hTxDwwmSqTCu43Um/WLYbNQdypUmfp9FKGhRzDUolZpEo36J7XI1hXydeCLIhbE8yVSIVCpMveYQiHwxPfEZHtYNz5K5+dmVCasGdEuT9F/UGNcxO7XXfGgwohiMSOvfwR5wPdkS3dyjaDqydTGQDOVKSxMMBDDwCPogHQkwV27RbGs+uVDsvPOITOf2rclrhqg/EF97cuq6Lo1Gg1AodG16+o8MT2tOd8acsZBv3Vrf0WLqCVnw1OjGk8a5uubIkkwLh2MyZfyii22hJRPTe/ovJaYeel04ted5tNtt/H4/hmGwKa74/mXIYcBUl5hnKm0hPoNRxQNDioYNLqy4YeuO5r050dDe0avYnRbn7OGlzhpSicFmONq5m3fhcd+lAesazXhF8601jvqgKfmOpbbiaA4O9igyAc2rE5o7+kU/NV1brzcFcZXXq+s+RH9E8e2tkpH62ymJxFobYp8IKPYFYF9WdKhRnxDaTxY1N3YrpqrwWc7jrj5F4rLtRdK6soyHhw0+z8nP6wlLI9Ty61VxRIe7fNPR9mSK2nI1vRExK1wtdqUUMZ88t0eGJbosE1xtqlnGxecCiH75VEFqIL+oZQtYmSjf1rs6rZqseIyVJCppT0bxyYLG1ZrwQAy/3+TY0VkefWIHL71wilbLWYk9evxJMdfMTJcIh/1EE0E+/HQWp9nmqSd3XfUaeL6uOVv26M5E8LSQU9MQQrOyjwTSmTAnTy6yc2cXgYBE+TQaDvl8HcM08FyPQMAiGg3wxmvnsNsujz6xk0x3iM3b0niOplJqkc/VUAoSaYlgchxZtQ8Np8hmI/T0xDhxbI5sd4yXXzxJLBbg6Wf3cvrUIvl8nW9+6zpKxQaLC1U8D7Zvz9Bsu7z/zjjHjy9w972b6R+OX8xN5cZwsUYmK1OyUycXOXN6iZ7eGOlMmLNnlpiZLvOv/6vbMA1ppHr/vQkeenQ7771zgWKhycxMhXfePM8DD2+j1ZIqzZ6+2Mr0F8Bue9xwYAClFOVSk0jUTzwRxHFcLMtkfDxPV3eMesOhWGxSLDQZ2ZRmcb7KkcMz3HDDANnuMLMzFUxD8R//5hM8zyMU8lEoNPD5TB55fAdvvXGeSrnFd763HzMaoCfs2/Dm/UxRU+/ULWvk2pINqUtW/FfC7b2K16Y1ezIQj0tG66F3x7nltmEsS/HAI9toNR3CYT/1uk0gYFEp25hWi2DAwnZcanWbWqUtjVmRAMoAlObVV053pvQa05T2qGgsgAYKhSbhSIBisUGjZRMPJtCud8XftRk7SmXwbmJRte78bzY0bxREPrP2PeKiSx1LDc1789IiuJzpmgmKRyAVUMQsh1rL5YOxGk3bxWm3yMaDHNiUBKA3EeD4TJWG7eEzFdmoj6F06Box/QrwtSenjuOwsLDAwMDANXL6j4zpQpNiw0FjsLlrPaPoCamOi/xS5JuaI0sQtDS39129Ux/EAPTQ0EaTNc2LFzSGknD2iAV+HKYXChjRDI7ykQ7QWRVfhXHA0/zk3PLkTz7/4hv/N6c1PhO+t03I3vPnPXamFE9tWv3+Pzwjk4d7BxQvT2hem9J8ewvr9LAKeHyDKUjFlkimwaiSooJO4ortgrZkUndxpeNoHN6dlciUzWuIuM+QeK+xsubnYxq/KSTrYnfvTd2rf7+zTzFbW53a/HpCsyt1aazW5XBdRrE3I1PjVyY0jicTyuVUgk8XNdmQ5p0Z2JJULDakScz15HW6XK7sxRiMihzgVxc0N/ds3K7keS6lUpFMJrNCTpeLDK4WflOI7y/Oa3YlJW5qti7GvwsVsF3NwW7FyaLms4rBnQ/v5De/OMqxo/M8/Nh26jWbSqXJ0HBSmpqiPrZsy9Juu+SLDW6/dZBIOkYm+cXEtGprjuUlE7bpavYNJAgHfTRbstpXsEJMQ50mpF+/cJJ6pcUjj+/gnbfP08w3OmYsj2DQ4pHHdzA5XiAU8lGrtnnphZN867v78IVMLBPCER/Dm1JUyk0MpZifq1EsNunqitDfH+ejDyf51S+O8/BjO3jtN2dptVy++dx2Pjg0ztkzebq7Ixz9bA6Ax57aRSod4Mins7zx+gV5zAoymTDuBk/e0+D3m6RSYYJBH82GPM+Z6TIz02V5niGLaNTP/Q9v4xc/PbpyI3D3fVtAa+bnKgwMJmi3PUrFBplsmFhcgt0XFqr4LJPFpQqZdJSZqRLZbIje/gS5XL0TkRWmrz/B7HSJZtOmrz9GKOjj+LF5fH6TGw8MEAz6KBabRCJ+fvaTo1SrLbJdEfK5Op6nabUcXvn1KR56dDu//PlxfvTDI/zl9w+wP2ZyccRZhAaD0SDvzkm+ruPJRqbrCubF5cYnuVGREyBkSTScq6HZsHn8yV386Ief8cufHyebDXPd9X1cf30fS7kGhgl9fQkKxTrHP58n2xWmqyeGaShMS9FotkmHw4TDFkc/n2NxoSYZpqai6WqCQYtsV4TF+SqbNqXJ5WokEkGUqZipKT5b1KQCUo6xO71ypq4g0ckNvhjdIcVYURI8HrhIepNrSqpHzdEYyPVx7bVjOR3h+EyFXKXV+f3w8BybZrXA8MjwyufGghYHRxO0bI+Az7iWcfoV4mtPTq/hj4egb/Vial3FnaXraT5Z0hSaIu7Phr7c3ehHC6I/23jlK5PXUkvzu2nRMfq0ZkBBNgCeIVNN21PsTH3xzzIUPLfl0ulsw9H4DVhoCDHZmRLi52qprxy4qLL0qU3iBD3QmSqmAnC+AnG/7mR5ygQxusZZPlbWnCmK3rbuSLj/SxPiJs01Jb7qfFmzwTCajsF55f8XY3NcJqELDc0LFzQPDklqwkYYjq1dySsajuYfzmj2Za++JUqh6I/At7bCj89q7uyBtqd4ZUImzkFLpAUHu1cnqcuPbbamublntd/+StiWlAzYd+c00wHYl7m6hqUvi31ZCBhwOCflEBEfXN9lcLLoiWmlG+7qk7iuTZsjPP3cXgrL5CYbZnRLmkK+TqPh8MZrYyil+fZfHqSRSXGm6uGvwaQtk57eMBsmHtQcya69o09xQ1ZhmeDD4jt/up8f/+AI9YYNSqGUkIVv/8k+Pnx/HLvtki80+OSjKZ597jrOj+WpVFpEowHCEckw3X99P40O8Wu33ZXUgFrF5uc/PcoTT+/E5zOp120sQ7F5S4ZCvsaZ04uMbEqxd18fM1NF7rpnlL7+uJRMHBxiy9YsuVydnbu76e6JEYn4+PyzWd5+8wLLlw5f0EfPQALvorHpeEXzzpzHHekoH38ywXe+J8+z2bSxLMkdDYV98jwPTTA4nOTBR7fTbNiUik3KpQa9fTG27eiiUm5Sr7cZHE5Qr9lcOJcHQ9HdHQXlkUpEqFZa9A8maNRtTp1aJJkIsnlrhmKhTrvpEI1KfBFaUS63SKXDBIMWjuvhuh4ffzDFAw9vY262gs9n4Dhup8BA0Gw61Gs28XiAcrnFwnyVtBOiK7wq5fA7NWof/4jEge/wwGCEUwXZmgStjaOolhufVlrH0qEVI5ajxRiI1iSSQT54f4K//P4Bxs7mKJWahEI+KlWbWMxPLB4kl6uxtFRnYChBb1+MZkMkKelMGO1pDENRrbR45ddnUapDiA0Dn8/g0cd3UKu2aLddwp270EjET8BvsjkJ2+MS+fbGtPz+393PVUtpci257p4saHZ25EyOJzdpvWGRukzX4GzpUjmM43osVVoYeIR8Bt3xIN3hEPNzNeLB9bTJUIqQ/9pg66vGNXJ6DX80dMX8JMMWxbrLseky1w3G1xHWi/HBgiYZgIPdV09K646m0haydaqg+d6OK1/IEgHFbb0yrRsIK3RVkYkqLEvRdCWyqCcsK55lNBzNp0uaoYhEi4BcoJZzPqu25kReOtsbLusmO8uNIz85Kz3qy7FYnhad10JD+qbzTVn13zugOJYX7Wq5rTGVmHBW79BFU3ZdRojpU5sUc3WpIby9V7SjIPmiveHVx388L8TpXEmI/44rxMrc3Q8fzCuO5zW/mdQ8MsyGeaQXI2TBbX2i3Qp9ySvNeEW+5sMFeU0fGFSMxjeOv+kOKb67TeLHLib7V0LAUtw3CKeLUh96d//G5O4PgUZKAlqulAQ8NLT8ekuu6khMcjmDlpwDlqk4+vkcN9zYz3tvj9NsOdRrbcrlFqGQxZ/+5Y0kIwaRsDRg1R2ZmDdsec03ivgqNOHR4fVxVy6Q7ovwF//yZuamSxTzddLpMKl0mEPvXmDsbB7X1YRCFjcdHOSXzx/HaXvE4gFOHJunWmsTDvuIxiUuavneo1hoMGplmJos0bZdCrkGtq05fmyOkU1JMFRHr6qIRHxEon4Gh1KUK01aLZt0JoJq2TiOR29fjHQ6jGkpfvLDz5meLq08/lDIx3N/sp9I7NITaySmGImBgUV8fx/Hj87xl98/wMJClUJBtJy9PTEWFsqkMhGSqTCv/fYcpqkIBn3MzZT5+MMptIZkKsgzz15Hs2Hj9xsMjSSw2x5t28VxNI7jkkqHWVio4HkyyU0lQxw5MkNXd4xioUQiEZJg+mSImPYolTzq9TaeB2dOLXD7nZuY6pQKmJaBs8GdYqnUJBzxUy63yOfqxONBDMVqOkP+PMW3/j3dowehey+jCZioikY75lfr9MgGkFtorOSFLsPvN3n0iZ0UgiG2JmWSGo742b27l1d+fYpbbhshEg1QrUppQCIZ4tSJRWIxH0PDCVxXM3YuT7XSZHg4RSTiJ7dU42c/OYZS8MRTO6nX5bX1+Q18PotA0OKdty7w8GM7+OSjKQ4cHCSZDBGO+jo3HbLVemxEfr9/Pqbpj8oN3ZXgatkc3dmnVq57IKfpTVkPy1D4TYOao9mblgKV2WKTfM3G8zSlhr1yO71/KEbQZ9Jqta74M6/hq8U1cnoNf1Ts7I1weKJCs+3wyYUiN25KbkhQHU9zpgR/uePqiOlYWVzvZVuvrGm/MarWaUovh96wXMAcR5FbI0ralVI4nlR0PjYi5pt8Cw4vSs3jazOaJzfJqnSuIeRpMKIwlUSgbE7IWvdUUQw8LVfIqHQ/S7bnMv7utF4hsT5DHO/9YTHcrIbQK96bE4nDDWs+dlM3fLqkV47Vi+MeDw5Ke1axvfpcPl7Q/PCMh4EQo7YnutkjS5qFhmhcd6fVuvX+8s+4uUeipMKW5uUJIahNlw0+d/3X7Uh+4eHfEJ/lNL7OujEZkL8/umFclyBkqZWigS8HMdRlO+v37++Sj22Eqi2O3quZ3DieaJ3LbTG6DUXhwaHlc1mIQsCSn7XY8Li1V86LaMzP3ut6efP189x40wD1mk2p1CSVCrJpc5p226FatTtmEYlcC1vAFRJrBi+adNcdzWIDDi9pDGVw//YsvaUGhz+dwfM0A4MJsl0RTEMilSzLoFRo0m47tFoOlYqkCDz86A4OfzKN53goQ8wtiUQIn2GwtFjD80RX+PFHEzz0yA7OnVlicrxEMGgSiwf57W/O0Wo67NrdzW13DNNouPz0Hz6n1bbp60/QatnMz9d47tt7+ea39zI9WSKXq5PJhBkYTBCOWldwdKuVXMybbxtmfq6C5TfZuaubaNSH50EkEqTdrlOvtXj8yZ28/OtTlIqNlcdtGoo77tpMqSTlB5ZpoDVYloHPbzA7W2Vhvko01mZoKIljO7z84mnSmRAHDg4zMVGQyCsF4XCAdssmlghi25rJiQKpdJh91/fjeR7BkEwNlyUTFyORCHL6pPwypzNhfH5jJZ3B79Qov/HXgNRvJp77b5gq+tjSrnL+s/VTUQ+ZmF5MTEEm3y+9cILbv7GP69JyTbb8ikQiwEOP7WBuRsTSvT0xIrEAhqGIxwMUCg1sR1qhHNthZFOKaDTA/GwFT8Nd94xSq7YJhHwMDCUoFBvYLQ+/36RUanD9jQN88tEUN908SLYrgj9gEI6sf20Vch28LgMvT8jN9e4rSGxcD+4ZUAyGNfPlFjlP07RdinUbu6M7MgxF0GcyrTVn2u46p72BXAV6EwECV9sNfA1fKb725FQphd/vXwnvvYZ/XAR9JvuHYhyZrNB0PBYrbYY2yII7lodtV4yRWsXhJU2uqbmhS6o0r3aFfCkUliVv+svYFIPP8/DCBY+mC1viYqK5PiuaxRcuaKKWdLS/OqF5fBN8NK+5b02+6sUEba4uerC1JOehIUVXUDI335mV1fWxvOYHZzTPbVYrrvxbe6HUuYHXCFGcrS2HVQsW65AMSgLCcmmQz1B8f5cQpXRA6FGlDXE/9IQVIVMmlT89J9Pq9AZGpkwQDs3JCvzQnARlo4WEf1U4X9aMl8X0ZhkyUdyelPipH56R2lWpSBUC73iSZ3ulZqarQSoox+JEAXalYKNz4fVpzdbE1ck8LENxXQZ+M6U52A17M6tvcCfyMkWtdZKcCm2IdKQnvqBFKh3iwMFBatU2rbbDwGACn8+gXG7y5mtj3Hn3KFt3d12yzr5aBEyZuN7RqzhdhKQPPjyT46P3p/C0JhYP8MRTu3jpV6dYWqrR3RPlkce3U6200VrWtLF4kLffPE+rJS1V6XSYYNjC8hnMzVTJdkWof9SmWGyyd28fHxwaZ+91fXz80RRbt2VZWqyRyURIpUNs39EFhiIQMnjymV0rJHRoJMmtd2/FjFoELNi6u4sdhhAPz/OuGDW0jLW5mIYCt+1RKrZZXKwQiwXYvqMLf8Ck3XZ54undLM5VqVRaZLoi9PXFiEYtbEdRrTQoFBukkiEcR0scUtjHgYMDnDuzxI9/eITcUp1kKsgd94zy1htjkl+qNa2WTVc2TK3uceLoQicpIMYnH05SKDYYHk5y+92jK+1VlmViGGpltR8MWoQjvpXpeXd3lEjcvzoNzZ+nOX4YAKdWZn6uwa9+eYK1hbbLU9FsT5ipucolxNTWUtNZaLm081VSWSl08IBUNoLK1+kbSODYLoGAj2q1RbEgxHf7zizFfINKqUlXT4xA0CfRUV0RGnUbn9+grz+GaRhoNNlO8Uqp1MJ1PXw+k8ef2kU4bGFaBj6fednX1mfIput3U7I5uFw2tYGGVoMP55po7aFR64inBrQHjZa78jFTwWA6SMRvEvSZhAPmOqnPNa7wx8XXnpz6/X4GBwf/qR/G1wohv0l33M9EvkmuejE5lUalmgPfvExQ9MXYFBPTzdWSUsfTnC1pDCVf0fbgQlmz0DC4fzBD2pLHAZIjeG+/5N8NROFHZyQr9UJFPjYUgYO90lrlN+HFC5IRulCH3ohmoiLkai3W6kWX0bfGeXtHn1inn9gE8w2Jh2q4mseGFcmA6nTMa96Zha6g4pHhFS8Lx/PiYFcoYn6Ni4RyC9lUpDvmAQUrMoSBNcR2OCbk6YYuvUKul7ElATM1CFqKZ7dInuyPz2qO5jVPblIczcPe9KXh+18GCtHYKgX7+kWLuWxC2p2GYznNfz4HQQNa3mqF7GhcZA+398oxKrRkEp0NyZ9Tl00NWP25npakBJCIuUwmu+5zbuwSk5qphKgnApIF2XCkzEAjU2+FPP5iSx7nxROeTEgKDpalEWvLZAwD5mZKKMMgEDRBgWM7eJ7BR29NsrhYI5ers8O4JD3pqmEq1SHgkA1pmg1XHPjLGZp1m/felrigl188xcJ8ld++chbDVGwaTXHgpkF+9Yvj5HJ1wmF5s67X2zz93F6CAZO//ZuP+Bf/6hbabXmAh96b4NY7RtBac+fdo6Kt9Bvs2tNNNhtmdqbM4mJV6lAj1goJbTmaE3mXqRmP67sUPSFvw+dcteU1uVJ+p9Zi8DEthdYepmFQLDRp1tsMjaSo121aTZuhkQSRiJ9qtQUKbE+DoQmH/XgeEnkV9TM8nKRYrLO0WEej2LO3l0w2TDIVIrdU5bY7N5HP1bHbLolEiPmFKh+8N0mj6fDU07v52//pQwJBC5/PYGyswH0PbVnJYq1WWqRSIQqFBn6/ycOP7eDQu+OEQhbf/t5+/AGTYMjC1Xrd1BQ06sBf8csfvkMg1A3G6kZKpqIn+e6fX08h37jk+PiUwkZ06a1KA2NNoYNSLsGQiWEYFAp18vk6yWSQ4eEEtu1RKDRwHI9kOkQwZOGzDJooKpUWiWSQsPZTLDSpNup0dcewbZfZ2Sp+v8HO3d34fCamYZDP11mcLBMImldsO8sExTj5n8ckRm4tWo7H6bkqpbqN1hqFhwKCPkV3LEgiZJEM+2g7HtPFJn7LIOw3CVgGkYB5xWvXNa7wx8XXnpxewz8NlgP4q2tqBAUylYz5L39XfDGSX0A81sL2hPz2hWV6pYFSW7M9qdgUh/c7a3PtQdWBnrAmYCkW6pr35mA0BmMlIbRtV7R7kZUJqaIrJE7z16bFqX6iAKeKomsajQuReW9OX9aAtHwMlp9XMiDf99VJj8WmrLg9LRPTiE+6r1e/QgjTrvTyxxQDYSHK6Q0crRvh9l7Rdv3DGc22pJQZLBsq/IZiR0pSB/akRXLwp9vhb09qjubg40Uh/HvTV/1yXIJzZWm2ivrgt1OSzrAMA4VCzGsPjEjD0nBMkQoIwduZVDx/QZMOiO646cItPWKIeGbzF5FmxeObhExeDv0RxTObYaoKZ0oSbZYKiLliV0rRcMW0ZhqKpQYYSsjyxT+31NI0bCgpuQkROcfqn4MhPz/76VHC0QCRiJ96vU21LLmicHmH+u8DpQxmZwpiQunkq5qmwfxclUplnIcfE6OQ18maCoV9vPv2BR55YidLC1VqNVtannwm9XqbZlMxMBCnkK/xvT+/nldfPs2Bg4P86ufHCQRMuntjDI8kOjFCHv/pP3zK0mKtYxjSPPudffT2R7A9mWbtSRvs+YLzqeXKVNtQmi0Jxe7U5c1tLpDpChEK+1iYq1KttZmeKtM/ECfbHcZD8jlDHVf+8mE2fIpQyCIcSVCvtZmdrRCL+4lFAximorsrQiBkkc81cF2olFsMDMbx+Qw++mCKD9+fwnU9bNtlcjzPN765hzdfH6PZsDFNxQfvTbLv+j7+4vsHWFqoUS436eqJEo8HVwxjvX1xgkGD3FIdfzzA+bJmtDbG4plPATBjWZqVIO3qJL5AEm2s30gtG9ZSmY2z15QCn8lKoUOEBjVCtByw25p22yabjdBo2MzPVcgtSSuU47iSRBD202q5VCtNlNFp3TMNTNMgHJZkhHDEol5jtRlMKRzbZbFQpd12OXp0lqHRDLo7RaGhVzT6F2NrAg4vwduzHnf2yY33UrXN6bkqnusCmqjfYCQboTt26cUv5DfZ2h255OPX8L8cfO3JaavVYmZmhv7+fgKBq3wHv4Y/GO2O7sfTGk/rdW/gfVdhtvl9cXhRmkHWhttvisvEM1ezidh5Upk0JdeH5YlbvtyWatBsAGYbsirc1oky2hRb/T7H8x5nSnCgS9bQ78xq/tkuRbElf35rVpz7+7OK/VmJyEoGrmbSqJmpwR0dTeVcXVZQB7sv/cyBi47dUlN/qfgjy1Ac7IFUUOQCb89qHh1ZXZu/PaP51ppphaGEsP18TPSYTWc5MPP3g6UU3SFx2vZGuMSopZQi6tPE/IpnNis+WfB4T9KG6A0rHhtWnCzCo8MwVhY97a6rnOZG1kzeHMcml8uTyaTBsPj7M5o7eqWxLLVymRAdcqEl1DJiSbOP1iLz6LlMusSFCp2pnPx9exLGSprNCYXneQwMJQgFfdSrLeoVmeAtP/xQR7f3+670QQxxy1NGQ0Eh3+D8WJ5HH9/BSy+ewnFF97gwX+U3r5zhiad2cfzYPFMTRcpl0ZQMj6Q4dnSOm24e5sP3J5iZLnP3vZvp7YuRSIY4fmyRZCrAM8/tpZCv86d/cQPlSpNatU0iGcRue/zm5TPklmp4WlPuNE/9+IdH+Of/6uarCmBfRiaoeG6LNJWdK0tSRdQnU/xsSDFZlSn68u+8CwQiFiPb0pgdmUCu4TKVczhR0Dw6svq6rT0PAiE/rYZLvWETi/oJdWKgSoUmlt8groN4rkcyGcRxPNotj7FzeT75aBrTVNidgg1lGFTKTb7xzT3MzVWoVaTtyjQMIhGLZtwhELIIBi2CQZP+wSSGofD7DZoNm1bb41fnPW7P1Gi89e+I+OR19CdTnC63AYVbWcRMDaAviptaWqyxc3c3fr95yWq/5UI2KoUOPrtK7eMf4T/wHUzLj+c3qNc1+XydRCJINhvF9VwsS9HfH6fZdLlwbAHHcRkcStCTidG2XVpNh2rNxnUcIpEAtaqNaRnEYlJnWqsJOW/bHq+9M8nCUoNd14eotuXaWLX1hmkrPkOR8GsGI0JM247HyZkKaFckNYMx4kHrK13BX+MKf1x87ckpcMVmlWv4x0E0aEqsiJb80410p1eDz3OakCUr6mVzyJWIiM+Ekfj6jykUT2xSVBuKjy4IeYgGZcU9XpFpZdOFhpa4qL87pUXTBJ0Vu+D9efjT7autQbLO1xyak+rT2/ug2JJKUMcTVz7A06Mbx72AEInfTGm2JFYd7/0RdVVu+eXnm75CzuHlsDVhsPWi4rRyWxO2LtV3pgIS2n+2qJmsiRb2y+p+646m6YqJQaB5tyYfXysv2JWGD+ZXv+7GboMbujW5hjjuv7VFrTh5R2Iifai0OwUBXyKIfPkxgBD2R4fh0LwY4nanV4nsqsziUnhaM9vRF8f9cqPzyYJmvi7xUst5sFsSiiNLy5W1EI7I+vbHPziyrmI0FPLx7e/uv8QsstHjnq6JNvnSbEgxau3PitbY05BMh5h7v079yBwPPLaTRrUl2ZeuRzQW4K03xpifr65oR4KdKtLcUh3P81ZyQ9OZMD6fQW6pxujmDB+8N8HvXj3H1m0ZhkaSNGptLowX2bevj8OfzrAwX8UwVEdSoXFdj3bLYXqy9Htpav2myBV2pRTzDSnECJiSttG3QWC953lMVTQfdook4j5J6Ci29LptjN9n4rYV45MFatU24bAfUynabQef3wcGLC3UqNds/H6TI0dmuPHGQV745TH23zBApdomkQjiuh7BkEVXV4SPP5rCcTSmqdi+s4vfvXoWz9PU6zabt6Tp6opQq7cpl1qYhiIQMKlV27z1xjkeeGgHz2YMrLkLLEweXrneufUiyc7J6LUbmE4brPXX1WQqRCBk8egTOy8xRQUDJg8/vpNAyISF81RPvElw5zPkSzbNls3IaJpmS7O0VCPgN8l08lhnZiokU0EGBhNUKy1aLSHw7bZNMOijpzuM64JtuximYnKyQDIRoVJpMTUprVu1ahvb0/j9Jr5UlJMlj56Q4lRR03I1W+JqZSO0jLv6JHbPMqA3JDdwWkNXzEcitIF26ivANa7wx8M1cnoN/yRIhHwMJoNMFprMFi8lp5W27lTzXZlQDEVhuiaRSC0Hci154916GZPO9VcIhH9nThM1YH9KcbQkGaG701IR+GqnCtRUQjg/WxIH/pvTmm9uUXye6ziwO8R0rKz5YF7yAoOmaBlP5EUb+cyoSPMN4GxJVpKJgLQiXewE97TGcSUW6ssSPo2sj7+qYGhPy4RmI/IZ9Sl2paUr/oN5Wc1fLRqOyBQCpkxed6QUIUukHRfrXo1OjVHN0SsEUaHIhqQK9CfnVh33Ha8DJztNMVf6+W2XK7RZSVzVNzbB0Ty8NiUZsgm/aERBboqSfnmDXGiINrZii1PeUvIYGo58jathMLaqNe0Nw3hA9LW39ICLorc/wj//1zdflUPd8TTzDSGjs3VpCOoNKzZfdBNG52jd0Q9vzWjCpqytR+NRlM+kUmzwwdtjRKIBtmzNMDyS4kc/OIJpLR91IaaPPbmTTz6aWtFCggTad3VHQEO51Fox8BiG4uzZHHNzVTzPo9V08PlN6rX2JY/MdTXKUH+wphakWOHPdyg+XtQcy2tu7V3/7+/Pa27okhu9p9do22vTkq5xS4/83TAUdtPPT3/xGXZHQ3vn3aO8+/YFbr51mLNnptm3r49YPMDsbIVQ0OLgzcN8/tksN940xLtvXyCRCNBo2PiDFo8/uYt6w2b8QoEtW7PYtssbr41x863D/OynnxOLBZidLrNzTzcKTTgSoNqwaS24HP5kmnsf2MrcXJmubJrihz/C37MVt17ErSzhVpbojjTwB3y0W/Yl09PlmltXazLdIb713X3Mz1UoFhokUyGyPTE+KRsMODVqh39F48C/4Rf/89s4gRR33DHMD/7jER57cicDg0kCPgOtJJPUNA38foN8rkar6bB5W4apiRKpdBjbdnEcj2bDplZ1KJYajIwkWao5vPy785iei9m5lkSCFs89sZNM1mJ0Re4thQDNNSe9pzVn52v4LYPHhwPUXYVlGmSifhbLLSrNK9+6XcP/OnCNnF7DPxl6En4mC02atkvTdtdFSk3V4OyMxm9ors8qejaYfMClkytPywTzZMFje1Iioi4Oxt8I+aassbf4JBfyrj4hnDtTih+f9Xh6VIjpmaJHf2Q53B5G44rZuuZEfn3U0RvTopccjorLPOpX3DOwVgsn6++dKan+nKxqPlyAOy+KQ4r4DJ4a1RsSzOma5pUJ0T0u13B+MC/xRVsSinxTiMpkRTMYu/og/MvhwwVx0f/7ExqfoXlui1pnQgmYijv6xKhwY5de10ZVc2TCbbvwySLc1iv/1nA0r05p7h2QdfmJvKzyjuclCgxketpwVjNI92cVPzuneWrTekJ5c4+Q/r8/LeRx2fRjKilUOF8WTejawa9GJpi39l752DieVKumApJnezwvJhyl5CYDoGyLIWowItKRbAiWb672oyg0PV6fkcejOxW8AsUtPfDSuMfLE/DIsFpZPV/Jod50NZ8syA3ZUESacg52X1oEcTEilmJ/Bg7nNFvjiuGExbee3rUySatV23x2eJZarcX3/+VBpqdLVEotYokAyaQYda7b38+hd8dZmK+KUee7+7FMxdtvXuDxp3by21fPyjNTqy1EKIXPb5LtiqzIA9bCNBWOo79STe2BLkXN1nwwD7f16BUdu8+AoznWxLQJ7u6HN6fh1UmPBwYVdhNeeuEknqdWVsTHjs1x8JYhDh+eZt++fmp1m2bTYdNoinDIR7Nps//6fkBz1z2jzM1ViEb8RGMBunsi2G0Px/YwDMXpUwvs2NnDJx9N8b0/u4F8vk6p1EJ7mt7+OPVaW3JkgYce3U6raaMMRbNtMJN5kqLZIpkM0B1p4H387/E+/vc89vi/4dcvnqbdWp2eLrv1JU5K46FWUgxGVGqlIcqqaFqL57H7buOlF0/Ratr4kiGOfj7Pwds289MfH8WyTLZvz5LKhFlcqjI0lOT99ya4MJYnmQpx7/1byGYj/M//v/cxTINw2I9hyNT2jrtHCUUs+iMW3/nufsanK7QrDbqzIfrWxF2tRdyvWL7Pmi02Ob9Ux+2cIFP5BumoH922KNVly1BtueRrbdIR/1dzEl3DPwmukdNr+CdDxG8SDZhUWx6zxSajXasC9V0pJSYTR/P6jMb1ZLL4ReYnQykOdGvKLcXxgkQTtT1xul+pAehkQdz41Sps7YKPFzS39irqjpiXJqsSJ9Ufhlt7FK9Mysp2OAovjWue2qTIdvJV862O098Hr0zKn3cmJTro7VkPpWC+LkR0OVR+KCrxTC330hzNjR6340n4/vakRKucLkqkVsiCbQkJZvd3WNNvpjSPDv/hWt66A/f1K8YqsqZ+fx7uHVj/ORGfRID93SlNd0jTHYaekERQ3dCtGIzIFHGZnC57gZbd9LvS8lxqec3fn9bc3gfvzi6v0uQcuLFLkgJembzUrXtrj+LWHjGQ/eI8bE3Kmr/lihPZMn4/km4ZioeGYb4merc7ell5E2040jIzXZPQ742SAX435TFdkwnrwR7FR/OavZn1n3PvoOJHZzSvTXncNygvnudt5FDXjJWFIO/PKm7v+/I5jH0Rte58WDtJW8o3CMVCDA7EKBfrnD65SCIZwtOaoaEEkYiPmZkKe/b2rDQ7+XwGxWKD0S0ZPjg0wYGDgxTydWzbleD2SotMJsRtd45Sr7ZWzFdKLU/kxTzjD1iX1dTWHM1H89Af2biM4XK4u1+KLH51QY75loS4vX95/lKph6EU9w7KhPyjRUV/tYrraJRStNtuR0vqkkqVuPW2EeZnK7haM7opRToTplxqycTYNEgkgoQjPnp6YgSCFtlMmKWlCslkmD///gGq5RY33zrCJx9NsWNnN+fP58XEOBgnk43QbLSxLCHztuNSr7f56P1JHnxkB7Vam2PnbOYvzKBdB3/Ax2OP/xuCH/8PBD/+H3ju8b9ioRaiXDfIbNpMd1+ClmVyvODxeQ6+vRVAraQYLOPWdI3cr35EsfcJGg0bywDdrJIrRhm/UOCpZ3azuFBjdqaC5WuRSob53atnWZiXgGjH8VhcrJFKh/jzf3aAUycXKZaaJBNBIrEAtWoLz5XJbSJmsmt7klOlFJ8UPIYqcOAK6q5G2+XsfHXFga+Qm7x8pUm+wkpclAJCVyh3uYb/deBrT059Ph+Dg4OdTMNr+GNCKUUq7KPaalFcjv64aEIYshT7MrKyfGtWc2cfXxgLdLIg08InNinA4Fhe8+aM5t6BtStuLVIAVwLeMwEoOxY3D2f4aNHkgSGZQH0wr+kKwudLQlLvH1K0PZl+KeBoXtb9awlku9MGBJD0S95lLCCff0NXp6oyJmajpisr5d6wYltC8bspvS6cfyO0Pc27s2JAur1PyNbOlLQN/fCM5sFByUX9mxOa4ZjkWh7Pr89C/X2QDip+Na65Z0AiqU7kNT88o3l2i1rRoSqELO/PCun48VnNuE9inxbrmrdnYG1zbcSSqKeWq1dIeW+Ylfab92bFjPbsFmmoOjSvmapqLlRY5+S/GA8NGbRdmW62Pc1kFYajGsXVETnTtMhkMkxUDSxTVvSelu93uijHsm4r2p68ucf9isdH6Mg+VpFvak4WNHN1eHBQ8faspmFr2p7coMTXDHeCpuIbo0KiKm2PqF9KIBxP8mlnazJVzjVlkmoarGu/+UOwNg90ZEuKC2XNRFuRm6hyarKKMVUlEfMTiQZIJoL4/aIZX5ivMj5eYOfuHonYyoTZc10vnqf5i786QD5fp1BogIZMNoxS8PabF3j0iR28+tJpGg1b2tUSQbTmspparTUvntfszSi2Jb/ss1PsSUve8NuzmtMlzQMDMgFfaGi6Q5eeR/vSiqWKZnyshuNAuVRf+TfTVMxOV4hE/Bz7fJ6urgiJeJBYPIBhKTLZMLbtUSw2iMeDZDIRTFNx9LNZAkE/n358jp27e+gbiJFMBklntjMzVcKyDLq6o6QzYZQS/Wm13CIQtHBdzacfT3HbHZso5GsYpkH3piHmCiamodFem1ffq/Dt5/4bgqWTACQSHspn4e8NUFUm5wpiyEoH4Y1puK1PX6IftwoXMMtT5ALtjoRHoT0HrWFmqkRPdwTPg5npEqdPLVAurZ+Am4YinQoxPVlmYCjO8WPzKAWnTy5Qr9k8+PA2xi9M8dhTO/GFTHwG7E1p9qbkMV1JG+6uqXTdNxglHfGTr7WZKshjqDQdIgGLnb2RKzYP/r64xhX+uPjaH2XDMPD7r43//6mQjfmYLjapNh0WKm164pe6S3yG1HcCvD6lCVhykR2KSSuTpdZnHO7LyGzsx2c1O1IyQWw4MinJhqTGc7Kq8BmS8fnUqOKX5zU7k4rfTFt8Y1SI6ZmiptTSGIaYWZZ77Y/mNLvS0iP/ozOaqr3eGFWxxWwixqWLn42m2JIJmmXIGjtsybTV01BoyXr30ZHl2cCleOGCZigCt4+u16F+lpOVt99U1BxNXwTu6RfTQNIvJO0PCau/oxcOIZPMxzcpnthk8OaMZrIiGahrsawVDVma57YqqrbIJQajEpq/Fl0hyXNdzhiN+6VO8JubFT8b0wxF5XluS4psodiWr3l3VnOqICT9gcHVogKQ43loTsxHy8gEoT+iKbXXmxpu71WXZGQqpXhv0SLf1HSHZCJ8siARNo8MK8q2rPOjvku/VqA5vCgVtn0R+dmf5TRKwdmynLMfL2ryLc0tPauEORlQ+E2JLVtq6BVNq2XIDVHIhG1JaSK7nInuD8HyJG0oKiv5890hPu7ciNkN6aY//Mk099y3BUMpXnj+OI6jOXNyCb/fZGQ0yfU3DHDq5AK/feUMAIPDSfbsFRHn0mKNRx7fget6/Olf3ECx2KRSbpFIBenvj1+29Wm8IsfgYlPMl4FlKO4dUExVxSyV8Mv5tCMl0/4V+YGGpfk6r//2HPv291LI19FaSKnramLxEMVCg0gkgON4TE4UGdmU4pWXznDnXaNEY35MZeALmDi2BPa32y4joxlmpopct7+P/oE4hqmYm61Sr9ts3ZbG5/dRLjVYmKuQSAbp6YkTCNSYHC8SjQV54KFtlMpNHFejXA/H1ijDoFxpE4mG0NpgrhxgaMu964w7y+pe6ZcXbfh4RfPSuGw3buyS3x2/U6P0xl9TLRfJbF8Nm9eeC9qlXgefz8L1NIV8g0bDXnd8DUMRjvolv9VvsjBfwfM0C/NVPE/O/WKhiUazMFdhcF2WqeKeAc3fnLgCOV3znPyd1qZ0xP9HW99f4wp/XHztyanjOBQKBVKp1LU7on8CJEI+euJ+Zks25xZqsuq/qL6vO6RI9mn8xuqbeMORadhbM3LBqtlioOoOiQZ1cwKyQcVSU4hpMgCTFZipytRpSwKGOrWO78xpbuox+WzB5qGuGil/lJZj8Pas5pnNindmNQMR1TFowXhZc2e/6hhyxMG+FoMR+e9iaMR5/51tasXMU2lrfnJOJkIRS9qXJqqan49p9mWlYAAlRqCWq/nFeU3NFj3m2indoXnRvT69WT52eFGiiixD0ROCii1tVvuz8vwHo+L+b3uXa1e6NBLKUIrb+xQ/POMxVoJdKcly/dUFmSj2RoRArdWaWgbkGhLpE7uMgTZkKnJryKmnNddlRUP5ve1qxTjkNxT7spoT+dUYpgPdijMF+E+nNX0RSUEwlcRt9YQlTuhUUepeazYMRTU3BNUK6btc8JXnueyPVIn2RLG14idnNXcPqJVzJnSFS4WnNYfmIGhJjNfRvGhgFxpijsu3hJze0g2vz8AtPesfxc6UxCI9PPyH1SZqNIXmclrDlyd1WmsG+mN0Ry3stkvbdnn/3XFuuW2Y1393lrvv28K3v3s98/MVyqUmma4IXdkIr/32DOfO5vE8jetqzp/LcdPBQY4dm6Nte2zb0U0qGwQF3YMxLMMA5WE74G7ogpKihz2Zr4aMD0YVQzGDs2WYqXgUWzBbcLELFUqFBpu3ZnjxlyewLIN4Iojfb+E4Lq4r7ViGUrRsl8+PzPKNZ3bzw78/wokTC+za3cPbb53njjs3US7XCYYs0pkQI5tSWJZCe5pUOsiFsQJHPp2h1XK4/sZB0ukQzabH2LkFisUmXV0R/H6LSqVJJBJgz3V9fPrxJLbtcuLEPLfcOkIk6uezIzPYtiQAlEpN0mkhzSNrAvQvh5GYYiSmOF/WfLQgGxgvd565058SsqAn2lwxVmnXwfTaGL4Inx2Z4eCtQzz25E5eeP4ErZb8cioFXd0RHntiJ6/8+hRPP7uXk8cXVo1vHZ11PBng1OkFcrkGw1sufpyK3vB6s+NaLFXaKDTpiI9o4I//Xn2NK/xx8bU/wq7rUqlUiMfj1064fyIMpUMsVmwc1+XT8SLxsI/rBuPrTEBHc3C26LEzLeSnKygEc3ty9Q3c9iSySdz7MiUMWkIE0kHF9V3rsywNoNFwSRQrzF9osC8TJGkFyDU8XppSPDgk+sGardmWlCmZp2Vyl+2s7R8eXj+xg9Up7vIkZtlsMF8X/eXax1C1hegc7ITp6w5divg0Z4uaY3kxEQVMTd0WMnlnn+LievfJiubxEbVSO9odEhPRVFUTtOD6LoXfkLapZEA0dcXORi4bEqNQyJLnGPFpPpyHh4cvzU0FSTx4b05zugjPbTF4ZrMQ1IApBPDmHokwUigOdMmk+dY+WVtvhJt64Pkxzb6MrAUNpTjQMaoETflvGQe6DD5b8viz7YojOSk8eGhIcUuvYrEha/L5BtzWu1rO0BPSnK9oFhqS4rC23OFydMfzPBqNBuFwmIWmwcGeVWK6ERxPjsd4VVNqwYEumKkpbA/u6FN8tKAJm3C+BvcNyApzJK4YKms+z0ln+DL2puFn5+CmbpnWLGdxfplopXxTJBDdIfV7RYktIxAyeeyJnbzwyxOUFmsAHPl0hv039FPI1wkGfaTSYrhpthwajTYPP7aTyYki+VydbFeE3r4YE+N5Nm/OMLQpRSBo4GqNqaFesyWRYKlGJhthYChx0Vpf8/asmOG2bJg+8OVgIN3y83MV6vkGW3uiKNfk18+fpNp0uWFPloX5KtVqi66uKBfG8jzy+BoJgqlwXY9IxMf+G/pJZYJ8789vYGmxilJw+52bpGvedkhlgvhMg0bdxm67uJ5HPtegVmszOJSkqztCOGwxP1fjV88fp153MA2FaRkEgxYPP7aDfK5G2/ZIpaMcOzrLjTcO4vMZmIpOM5OLzxK/e7vtkkqH8b6AmK7FaBzOFDVeq8b8b/+asCWa9/XGKhtdX0LFgzSaDtFIgKbl8Kd/eQNLizXRE2fldT78yRQPPrydI59Ok0yFVxIb0J0IsoifZt0hEulEW7D+wV6fVbw6IUOBiyHZ2Jqw/w+7aft9cY0r/HFx7Qhfwz85wn6Tg6MJTs5WKdQdynWbxYtW/Dd2SVQPSiahxwvyBhzxaYajEPaJM1+MHvBFkyIDWJiv89OfnwTXldW8X2OZcMeje/jWlgghS6ZgSslaFyDflBX/8jp9I/JmAJWay+xMhVKxQSoVIpiJcqFlcmf/Kgk9locP5zWPDCtOF6VS1fZEU5sMKDIhmK7Kur/UCXq/vmtjM8hDQ4pXJmQqCzI9rDsSgbUlofiHM5rv7zJWHPDLcVCOp/mPpzT3DSgmqpLxuNgQqYHtLpPl9SainSlFT1iC98+V5Pv/xY7V7/vRAvz9Kc31XbAnrXh9Wowo/pDuREGtR8yn2JeF/3BKA5KQsDWxLKO4FJmgpCUc7JbH8WrHdHZTt2Igemlj2HBcMRwXgphvffnc141Iqe1pfnpOr8gzym24LqN4cFDhM+Q5b4pBfxR+M6m5Iat4Y0ZzXUY0y6MJmUTf2itNWNetnQoq2JwwaNWcVeLWFZEoqS/IOK3Ycl6VW5q7+9WGAeZfFulMiCe+sYvxCwWUkszNVDrMxx9OMDdb5Z77tvD+exMUCw3K5RbdPVEeenQbkUiC+fkqPp9JOBIkFLIIRizQGkvD7HT10izXoI9vf28/qb4Iny5ozpaluOKbW9SG586XgQHkFhq89MJJ8nWHsAV3372Zl148hS9gETUV11/fzycfTaM1VCotDMPgw/cneOTx7VTKbapVaX4aHEryi58eJR734/NZhMM+EskgAb+FUmBZBoVCg+mJMvFkkGw2RDQeJJUKoYFG3cb1PMrlNv/w90eoN+yOwUd094lkkFdfOsU3v3UdF87n8fst7rhrFMsnxGxutkK91paGJddDdW7qurqjXzKLUzEYhdbieSrnP12RJzlLY+uMVaVim2Q2RP+u3RimSaXcJl+oEwz56O2LEokEKJUaXH/DICdPzdPdE+OtN87LT1BCTB95fAefH5klFPKJPldxybncF1GYavW6shZNWz57eaV/Df/bxjVyeg3/i0DAMtg3GOPN0wV05+/rodh8SXapotjyyDWl8/2tGTEA7U7TmSDKFLLtakptaLii4ztZgLszHs//7CTKc2k6QjgdD6p1l3dfPcn3/vR6sEwuVMSstEzO4gG4q3/N5E0pLFP0ao6r8TyPc1N1XnnhJG3bXXGVxkImTz21i5gVwtGasyUR/z+7WZFvwXtz4jy3jNU1e8XW1GzFbE0mtxNV2Jve+MJctsFZ8540HFN8sKD5q13y2Jcamk8WPM6VJWvzpm6ZqFmG4vu75OdtisMyqf9k0eNoXvPatJQRPDC4/tinAhK/9OaMmH0mq0KyUwHFwW6RJ/z9ab1iXvnlBU02BE9t2phgXJdRXJdRlFqapaZMYkFc/TtS68nxrb1qRaIwEJVihMNLmsNLQow3xTT7u1anyMvYFAP7K8rQ9hmK72ztRCWxetxAXPQxvzy2589LTNa7c5qwTzTOS00xr4HIEy5u8PWh6G/X+Hf/4QjNhhgFNRLC/yd/ej19/REuLv39eFGz1BB5xc60YrDnD38DNwGnLeH4Z04tMT9X4YFHtrIwX+P5nx3jrrtHmZos87vfnOXW20d4+cVTKAXtlsOLvzyJP2Bx/wNb+PD9Sebnq/zz/+Jmxs/mKeQbbNma5Uc/OEKruV632Gja/MMPjvAX/+JmfKbJt7dcOWXjy6DVcFfisgzADPqpVlo0mza27TI6mmJhoUYsFkABzYZNOOKjWGjy21fOohSEI34GhxIUC3XufWArb75+jnsf2Eo0FqBWbeOFwTAleWBoKEEo5MP1PFxH49guhilrAJ/fRLUVp87M0+gQU5DrCQrKpSamabC4UGXzljTVSpt8rkZXdwzLUnz+2Rz1mk044sfzND6fwRPf2EW10iQT+XIEdVekRv6lf9eJWFs91s7SGLz8fyMTy9IdTuKOF3HPDxJ77r+jZik2jaYwDRPbdsS8VW1Tr9n09yZwteaue0eZniwTjfoJhnx8eGgCZSruf2gLtu1eNi7skRHxAGSC6280l1sFL31vuIb/LeIaOb2G/8XA8fTKkifsvzq35VhZakRBCEGxpXltSjSpjgfpoHzHhF9ha9Hg7UopyktVWm2XoAlRH9QcQMtq23M85ucqZIeTHF7UPLmGUPkNyRM1AKftUam0mZ8VzV1Xd5TevhiHXj1FEI+gT6GRyWej5fLT50/w4DP7eG1RLq77s4r/PCZr9nsH1LrA+c9ymgtlzQ1dirv7DRYaeiWiaiP4DOhaMxG0DEU2KFO07UnNTT1Ccp8eVZwuwi/Oa+7u55LpBMhEenNcIps8xDjxH056uFoanDbHO8aKlEyzJ6rw8JAiscYr4Dfl3yot+LMdipotRO1kQVN3NL1hmXBfHOuUCIjW9GxJYykoteHHZ1YnwiC64oeGJHf1yJLob/dmFBMVzWhcDFW/GNOYSqQG13cpdiQld/Srys+Ey5cb7EqLvvmlCSHXmaDirj4h6H0RxYWKXpnefjivV6bpy6jXHH7ywyMr07Vl1GptfvB3n/Iv//e3EIn5VqKsck1NpQX3Da6mJpimgc808LSH7egrkpVSS19SQGACSwsNIZtNh3Q2zInjC8xOV+gfSFAuNXnrzfPcfd8WtKdpt2z+8n93gHK5RW6phmUZWJbJa789S61m8+0/2c9HH0xy9kyOru4IgYBFtdLC7EQ3SLuP/P43Gza52RI3fsmWqImKXpMdux6ehrm5Cm3bBSXSm2DIT7EoSQKep4nFg8zNlkkmQ/j8JlrD++9N8NCj23nvnQssLdYol1u889Z57r1/C6GQwaOP76RSbWFZppQVKIhGAxQLDQqFOql0mGgkQLXaot32CIV8WJYiFPKRW6xRyNdXmo2Wmw50RwZk265oUHuEbPYPJEFJ5vENBwbZtaeH+bkKPb0xmdb6DGamK3T1Rb9QcwowW9McWdI80K1JHHyWnluf/eIvUmDRJhQK8/KLp9m2PcvsbJkbbxygrz/OzFSJyaki3d1RUskg4bCf3GKN3FKNu+7dTE9PjF/8/ChPf3PvZc9JvyGJH7+b0tzWuxqBZ5kGtgO11pV2B9fwvxV87cmpaZokk0lM81ou2j81zE6VoQbytTa9iS/ev4qzUy5ebVfzTidiqSskjvltCXlj+u2U1Nw9t0VRtg2OnGlILakjxDRqCaHzPHmzLBYaDG1O8dSoWmfwASGm5UKLudkKLzx/gmbHFJBIBLnt9hHqtTaWz6TpSgi+p+XNN4KLLlV5bCRJb1hW4Psz6zWQy/h4Qdzqy5ODgAGz9Us+TR5rS1NoStD/Wtw3IFFY/+mUhN4vu8L3ZkQz++txmVLe1L0agTRWlvzUhB++uUWMWLf1ygr/0WHFSxMSu7VMZlZlFOtRbUOxLc/bbygMn6QkTNc0PuCjmqavqjjYs/FzumdA8YPTctMR9cHfnPB4evPqNHQwqhiMKqZrmufPy5vc4yOKF8c139oiiQ2WoajamlcnNUcWNTVHbgguDl7fCIZhEIlEMIwvN6WptDWHcxrXU+uydd+ckRsNgJmakNapmsZvqnXSEMMwmJ4s0WzYK0Hja9Go20yOF9myLYMvYJBvaT5dFAOZ3/j/s/dfX3Kc6Z0/+HnDZGakzyzv4QqWBEAD0HsShiDZ3WqSzXaSWqPR0c5c7d3+A7+9nr3ac1a7oxmpLdWO3nfTgiBBEgAJwgPlfaW3kRkR7168mWWAKhDslsgesb7n8JCsyoqMjAzzvM/zNcqyS5YVHWB+rkRLW5De3jjBkAEaVxic52uSP05I7upeDDkAVSC//sp59tzaiyY0enpiOI5LteoQsDR+8OMb+Pm/HuM3v/qUaNRPe0eYS5fS7Ds4SF9fjInxPJMTOe66ZwPdPTE+PqoKU4BwxMf8vOKvuq63YL6v6xpINX2Yny+xTWvD/hKLiaOzKqr0cp9ggJIrmJ2rYNsOrqPM+K1yldi6GK7roUlBLluhp7eTw+8Oc/c9Gzj87jC1msuRwyPcfd9GkJKJcdUJ1DQNx/Wo2g5WwCAa9Tc8WyWGodHTG0bTDKR00TQNKxjGdV0cRzmNeFIyN1cmHrdUMQog1Z2sGVzguZJk0iLgN/D7DQr5KqapMztXwLY9RkfS7Li+i0+PT3DDjb0cPTLKzt3dyIW76MpIVSWfzKmQjDu6BfP1MOdCd2G7YAroCMH2xOrnfRHwW3Dr7QMcOTzCjus6mJkpUi7X6OiMMLA+Qb3mMjaao5C36eyKsG1HO1JK3nj9Ag88OEg6VSIc961aRHeH1HX6/rRkZ4ukNyQp286Ct+nXgbVa4avFN744NQyDZDL5de/GGoByI+dZIEmX6l9QnMrGK5VQqOSosf7uVtGwNVL+mR/PqoLrxnZVqIIg4Zds6LIY/kxQcVWx1SxwdU3deOIJpXi6vDAFNR6cmy0uK0wBrKDJ/HyJbLZKIhnEkWqEvlRVX8pV2DSYwPUU51Nf8RkgF2gGNU9yYk75qUZXcDGxXcnvLqm79Q8Gl/9O1wS3dQm2JCXPXJJ0hxbTsjqDiu84nJf8yxm4o0vFsX4yK3lsveJM/q/Tkh1JdaRbAqoTuDGqEqD2dCg/14GI4nBe3gGN+NQD8pkhyUBE8Uj3L1Gfe1LybxckhbrKMd+WWG4HFtAFf7NNFcdlR6VexVf4/D0hRUv46VmPbE0Vn7++KLkuqZT9YVPw7Q1wbE5yNrNceHQ1aJpOOBy5thcvwYUczJcVR3LpZy3W4bqkclwIm0CDanHdZbceXYPUfOmqnc7UfJlwxI/r9zEh/PSGBTNlSNuCllKJX/702DKbn0DA5Ps/2k0iGcAfNJYVqFGf4KZ2ODytFgJ72wVdYZ2J8RyFfJVSqcaGDS3U63Ue/fYODr9ziY6OEPGEn3/4b7cyMZEjNV+mpTVIT08MTYBdrZOeL3Lz3l6ssMnQhfRCYep5ktHhLHtu6V/Yh+ZHdV0Pw9BwXEkiYVGtuIgvwZm9vVPwyqjklk4VX9qEBshcVXGDczae6y1wSm/c04fPpwIBUqkybW2hha7wXfduQNcgn7cRQFt7mJ7eGOlUmdHRLJZlEI9bzM0W2LZDebsmW4JIKZmfU13naNRCaM0ITwfTFIQjFq7rYFkGui5UIEHNWVZPSgnBoEF7R5hMpozrSqLRAOVKjXLZIRg0ueGmXirlOrfcOkC1WmdurkSyJUhqukgkFsBvGcs672VH0V5sV33PzQVmxISu4CJn/PAUPDPksTEKO1pWjk32UMEND+4fZHa6gERj46ZWbLtOpVynUnGIxfy0tocIBX0IDTKpKvfet5HPT07T0Rn5QuFWRxAe7BM8M6TS4JoI+r+e4nCtVvhq8Y0nbzRVuV9mfLSG/xicmiyiHpGCSODKdVPNk5zLSl4c8fjtRfXv312U/PaS4pHe2S3YEINLefjdJY+PZyUDUXhiUBncNwtQKaGzM4I0NNR9bvHG7EkP06fR0RlZsUAQQpBKqQK0Wm08UBr/lIs1YjELz5M4jkvYFFeYsscT1jWoaQUZGyxTdSzHS4q3mL8sjtyTkkLjZ71h+Pl5yaX8lRtP+lWQwdHZ5b/blhAcHND4TsMu69cXZcNZQKnwD/QLcjVJuqqM39dFFF+yPwIzZbWtZ4eUsv9yeI1j91CfSs4aKy6Kq0CNxL83KNjRIrBd+HCFbTQFMEFD0BkUq47RAQ70Cw5PSW5sgycHBXVP8tzwopjr+hbBE5uu7IKvBikltVrtS4pLlGCtd4mAyvUkIwVV3IMKQ2hyp3e3LqaKLX19S2voKn0vSLYEmZ0q8PmHw+yKeJzLqmnBBsO5ojAFqFbr/PLnx6nXJXblypFoX1jw6DqNve2Kp3w6A/OzJcrlOoauU6nYuK4kENA4cGgbtbqnFlcCkkmLLVvbSSaDIEBoGvPzZTZv61DHTipPTAAE1GsuExN52tpCBJZe443DIKUkGDRpawszMZ67IpTjaugKCe7rEXyeUt3k5vlmV1yef+ZzQkET09AWO28CPvpwlAOHthAIGEhP8slHYzz5/V3kcza///VnvPzCWS6cm6dUtDF9Oq++eIaXnj9DOlXi/Ll53ntniIF1LQghKJZqnDk1y8R4Dp/PAAGmKfD5dOo1j0y6zOhwjuFLKapVl+6eKOfPzKn39yshVRNW0OSRb++gpdWirz/BwLoEAcvANHSiMT9+v47PpwOSTKbCB4dHePjR7WQyFeyqy7/+/z5iZqKAY3tkasq95KURyWBMBVRcTuNY/BoEd3SpVLVTaXjuklzgel6OZnBD74YEGzYnicX9xBNBIlE/3d1RWttCaJqgXncxdINE0uLll85y7sz8ZffXlc92y1B+07taBGdmF29+4a+pOF2rFb5afOM7p/V6nampKXp6evD7rzSAX8NXg+H5MuWah0Qjapn0Ja/MsZstK//KZICFbmnMr1KX1kdVp+6TOSV62tcvVvTKa2KirnH/ga28++oZ6rXFm41usCSD+kpoAtL5OtlMdfGHjSlaPm8TDJn4/QaO4zWkqBLXU2KlYEAnkIwgpccXuQkMxgXvTipP07/ZJjifVWKapXhtTImRdrUKbmhTtlRvTUjOZyX3dAsCSz7/WEHZUJ1OyyuMzON+waF1ao9cqTqkD/Ytj7h8f1rFf5Yc1Y28pUN98J0t8LtLiu+7LbGkW9WwiTE0NWJfGcqDtd2SPD+kiuO7usWyrte1otUS9IalspbqF+xqhXMX4FfnPZIBRXG4mrBmsiTpXjJed12HTCZNS0sLhrGKQeuKn0hwc4fE8SQnU3AmK+kPC/Z2qG1PlCQPJFbfjw+nPbb1xLAC5kKm+lJYlkFbW4hLeZvR4SzT4zke2dyCXxecOZm+ojBtolKuMzmRo6UtSDIYWrHo7ggKvrVOUMhW0FuCuK7HZyemuHlvL62tFv6ASamkjPhdR2IY0NYewe/TGR/P8sfXL1DIV7n/wUF0Q/DrX33Gkz/YRaJxLQtUvCXAO29d4snv7+LpX5ygVnMX6hO/ZfLkUzs5cXySltbgNXl2LkXEJ7i/V4VbvDEmebBPY2I8TzpV4cQnk+x/eAsvv3AW23bwXEkmXeWTj8f58U9uYmI8RzZTQdcF//DfbmFiPEcqpbrCLS0hPjwyyo6dXRimTrGgpiPRaIB8rkI0GsG2HQJ+nXjCIpupEg6baLqGrgsSyQChkEE2Z+PzGQgk4YjJbXet4/13hzlwaCvFok0uWyUeD7BxsJWWVgvXkSA8XFciPUkk4kd6kky2SmWujM+nYxhw7wODaIbqBiMV/eLn//IJP/rbm9E0iIQtHl3HFbZ3K8GTknemJA/0qQnKS2PKH7gjCK0BRZuoOOp+4NcXI1BdJLoBuaKHa9v4/TrxuIXnehw5PMxnn89SFTo/+KvF+2vWlhydVcl/K4dZqE7vyHyJnqBHR9S3YuPiq8BarfDV4htfnK7hLwOj6SoSjYBPZ0fvoqFh1pZ8OCOxPWVvlPCr5CW/roz1myOnc1nJWFGNTx9dd/UuGyh183c3BnniqV3MTBfIZirE4gHiST+hqG/FwhRUfKQZMEkkAivWlx+8rzoxh98dpuiogtAQYAV0Hjy4lQsVjU8Lkt6wJGIK2iyVfvXyqETXVGyiqSvvwRsa3qQagsNT3kKB00SHpYp02204ApiCR9YJfn7O46URyb6BRU/Vu3sFz16SfJaWdIS4Qsnu05QYzK8LnhpcNLlvYqygir6BiBJUNT+8oQnu7lb+qZ3B5dGy+/pVR3ZPu7K1ardYMdVIIHh0veB0RvLisFL494QUraHNutIaajU81KfxwrDH6TRsT2pEfB5bE8oBoeYpy6fV8OHMyt6KfwrGCoqG0RUUfGv9YoLUbEV1OFd7CAPc2A6ao/PkD3bxy58eX1ZsWpbBE0/t4oMjIwysS5JJVxgbydI/EMdGMNfwIV0NzfF7Mxp2JdgVl2d/d4pHv70dw9DI5aq8/eYl7n9okISm4XkesVgAK2jiOh52tc78bIHz51J0dkW59fZ1+PwaJz6ZpFyuMz2Zp6Mrgs+nU6u7GIYGEs6cngPgxz+5iXSqzNysGkn39sU4f3aW4aEMGwdbrsmzczgv6Q2zZPGhUpAsA9JFj9HhDP6AwdR0gULRZt/BzZRLdXK5Kv0DcZCS5585hZQQjvj5/KSK3Nx9Qzc33dzLxQspThybJBLxE09YRCJ+wluSOBKqxTrP/PYU4YiPJ79/PYGAj8nJPPW6R0urhZSSbLrC669dZNu2do68P0q+WAO/jxt3tHLjzT3c+8Am5maLWAGD3us6iCctTFOj3FgIBCxdeSbrGqWS8g01DYEDxOIBqpUahYKNlJJY1M+ZM3MEQz6mpwoMX0oRTwbpDZr49C9+3Odsj3em4IZWJeQLGZLdrapLOl2GT+clVUcS8yuHjbbLeggucDQr2Jyw0P2CSt0lNZ0nFPZx4MAgZiLCp2WN8ohHzAcTReW+0bwmcpU6mVKdrnhgQZW/MS54/4KkJwiW+Y0f9n5jsFacruFrR65SXxi1dcYCyzpcMT/cu0SFvBKmSpKZsuTWDkHQvJL/eDmWjpcX8sRFgnq9ztz8PJbXwko6mKmS5I1xybd6QszX6wT8xiLntPGW83Mlzpya4ckf3sD58aIqehMW7Z1hxmo6FzLKS9XQ4GhR0qDZ8u0NgoCuOIuluuTxjYKhAjQnWLd0CD6YkWyJLz6Ed7dprI8qzulkCQZjqgizXRVQ8PR5yQ+3KEFSzFRCi+uTgjfGJK5UJv23dwku5lRAQHcI9ver7V9eyJUc5QiQ8Isr8tw7gmpE/c6k5LH1iwejJyS4p1uJgTZEBSfmJff1LjlYl2FbQvGFh/PKGmwor3xfH+iV9EVUBGrEXP3vAfZ0qP3YmlCxtNca1/olpsdfiP6IWKYav5RXxvzH5yXf33z1N9KFxthYhpnpAn//j3sZH1OczmRLkNa2EB8cHmHT5lYOvzsMQCTiZ2oiTzpTprU1eNVtt7Sq0fuVBZ9kOA+eENiTBeZLDjOzBZ78wW5+95vPkDXJr352nL6BOPsf3ow/YOI5yo8znamQmivR2xsjEg1gWTqzs0XOn5snn6uSSlcY2Jjg3gNbePbZM5imjqapMIvhoQybtxa5eHEeIQRjY1k6OsKcOD6Nz6evSq25HJqAdyfh3t7lP18fEwwN5YklLCW80gTzcyVeePY04bCPYMhHKlXijjvXkc1UMUwNu+pQKtbw+XWicYt//d+fUK3UaWsPK55p0OTF50/zgx/dgGHpBEMGTzQ6wE//4jP27O0jHPETCpqkUxUK+SxvvHaB2ZkiE+N5ZfdUrDGXtdFjFpmySyLsY1O8FV2Hel11SQ1DxwoYSCT1urKKCodNfKYgk6liWT4MQyOfr1Ip19ENjXDIRz5fxe8zFpKZMpkqwZCfmeki6weTOKuMpHMNYd1kGXa3qnO44igR4XhJcHO7imIuOSq8YyV/Z1DUK8sUnM943NsrwBAL99dmGElHXLlmnM5Idq8ThBq84mrd5eR4Hul5TGQq9CQsOqJ+fHisiwgkgrF0lf4W6wubD2v4Px9rxekavlacmiwyW6ihCGuCRHD5CFWguodXw+Fpec2FiETy+piKC134WWMs5V3lQWi7ksPTku9uFIQMDdEe5tBj25aJopQJdojb7lrPZxUDJxHj/h0J6q6y8nlvSOV4r4s2DeYXraaa3NTrWiSn08oQ/+NZueAL2hdWKVmXj6ZjftXduKdbWRWtj8L9varDaWqS96fgnh6Yahjrb0uC7Qm2xFVB9um8EkcdGIBfnlPZ1jGfZP/AUlqEOi41F1j4elQaV9SvitYb2gRVF14YlhxaB80CstUS/NWCOOiLvx+fJtgch81xlaz06qjHpynltqB+r6JpdyQh4rtyIdJuCfI1yWgB1kWv/QF2lWbmn424Xz2Mf7zliz07NQGZdIWTn84wOZHn/gcHCVom07NFCnmbgfVJPj46RipVJhAwVIdsukilbLNxUyuWZa442reCJt09MUyfdkXB56E8gC1DUCtWEMBHJ2b57qOb+fHf3KTiSbNVQhE/dtVlYjxLe3sEf0DH7zPo6AwTCvvxmRoXLs6Tz9Qolmp4UhIO+5goCT6sWnzvB7sozReYnS2DlPgDBkc/GGV6qkDAMnnyqV2cOD6Jz6dflVpzOfojgnRVTViWThc0IcilSiQSliqIafiIIsnnbfJ5m0DAoFZz2HdwM7quMTujrLIcx+P994aZnSkCyprrof2befftS8xMF5mcLDCwKYErJZ3dIf7+H/cyMZ5jbrZEIGjit0zef3eYYx9PLuzP7EyRz05Ms35jkj17ezgx7/JhWvJg2EMAw3nBxoSJ4TjUapJioYqma4QaCSCVikM2UyFgmRimxshwFgFEYxaBgE6xYHP43RFuuKmHfF7Fv8ViAVKpEtKzMDRt1eLUMqAnLAj5YHtSHaPD08pVonMJnShkqJjl1TCUh0od7uhe/Fnz/roUuiaW3YMd1+PEmCpMBRLpuYylyoyllEWJCj6RjdS9tcL0m4C14hTWrCG+JtRdj9lCDYlGKGCwtStyzf6mTZxKe1j6avnwy9G0UOkOKd/LlbCaddDRWdiSUOMnD4gm/ARDJn/7X/con9O8TVtbiGRnhAnXYGdYYhkC15PMVSQfzEi2JQTXXcb3FCyPIj2XVcKlZnerGZNad5WJ+0ooOmr0vTUuGSuqYhRUgfdvFyT39Ag+nVfdRxDsbl38271LrJx+sFnwz6c9BuPwm4sSDdXxzNYUh/flUYkmJL1hwfmsxDJUWld3SNlT3daprJzen4auoLxKcag8aL+oUBsrSmqeEjvVPfAbKinrk1nJby6q1/x4y5Xb2dev0rIe0pRQ7OpFsSRvq4fz5fiyNlKrIekXJK+RouZJFjia6XSFl188w40393L+zNxCAlMk4sc0dR7ar2Iib9rTx5lTM/h8Bk/9cDe//NlldICgyVM/2I3UWbHg0xqetULAUEOwV82UqVZd7KqDrmlsHGxR6UYSXBeqdg3blkQiPrWok5DPV3nxmbPsO7CZQq5KwDIJh/2U5/Ic7LQIWDqJaIL1m5KUinXGRrNs29HBnXevp7MrSiZdYuNgC7fftY5AUAmKRKPb9kXY3SZ4d0pdZ7c0ClRPQiJh8fmn0xx6dBu/+/VJIhE/hYKNroNp6uw7uIXD745w055ehofmuWlPPzXbwfQZXL+zi57eOLFYgP6BOEfeHyGdqeB5krmZIhu3tFB3XFzAHzLYel0bLZMh/vj6ec6enV9xP2OxACCplWpcH9UY8QueviCJ+lw2xwXSU8lZ4YifatWjUq4wMe4SifqJhv0Nfq5gbr6EJgTRaADD1JmfK3H43RFuvLmXI4dHABqLF3XTCIZ8yg1BCJwVjqdPFwzGF3+ettUCunuVDulq2BJXC98vi+FUhVrdRUOyd32M+WKNofkKkobbAur73NHz5R00/j2xVit8dfjGF6d+v5+BgYGveze+kZhshrsLwY0D8Wv+u5G8ZKYCIwVFyt8Su/rrK47krUlJ0FDemNtWEaQYhklbW/vK+1pU9itNeIDm04i3WrR2BEEI3p90KdSb9kAC1/N4fRyqLqyLKJP4ozOSrqBSvd/Tszyvve5JxovKpXAwDtHGuMtD8saEGtm/O6XEA0sLrlYLLuahNyIYKsqFbuIfJ+RCHOEdnYIXRiQdFg3x2MpYF1GK8phPeQ2WaqpAfXVU8kCv4vranooObfpJ/vNpj8mSiijd1yf4eE69d2ROsr9PNGylmlA+quNF6IvAnvarczDnKstpBgm/8mUdLaqEqH89K/mbrcsN8XtCgu9vhl+cUyKOhwdW3/5MGd6ZkldYTGm6QbKlbUUP2tVQ89SiInANopPVIKWko7PB0ay55HM2n3w4zq4buimX6hQKVXr74gv+m5qAjs6w8hWdLCCBv//HvUxOFhp59criKRAy0fUrfU6Xv7eHGQ+TCOl4dY+PPxrnhht7CGmCoYtpzjmSnp4ICMHFc/PohkYiaTE2mqWrO8YnH42z7+AWPnh/BNPUeeChQWq2w7lTM7w/V+bAoa3E4n7GJvJk0hW6uiNsGmxFMwSulEQSPpBQLTuMXMqQSVdIJC06OiPX1EW9s0vwyZxagN7YpoQ63d0R3njlPNuva+ehA5ux7Tr1mkc8YRGNBahW6jy0f1AVbhtauXhBKcnzeZsjh0cIhnyMDHkEQyZTE3nchqArHPEtUH9UNw8cFwyfRqFYWzTWX4JAwCAc8ZFIBJkYyzI2kmPXjd1sigVoDahOf80BK+ijUrEBnbHRLHbNZXBzG7lcldnZIrqm0dUTJRQySc2XmJgoUqs67L6xhw+PjDA7UyQQMNh3cAuffDTOTXt6aW0PMTtdJBLz4wusdiwXz9svs6D6cyGlZCpbRTQmNJZPpy9p0R0PIIS6tj0pcT2JubL33leCtVrhq4WQX9Yr5T8Q/+N//A9+8pOffN27sYavCGemikzn63gIEiEfgx0hAubVV6ZDeclnKUmbBVMl6AwJpoqSm9rFqp26D2YkbQFWiD+9Nsw3Op+H1q18Y/SQvDupCrpdrYvv8e6Uh6HBrR0aOVsS0JXqfaigOKEXc0p44Ej4LKWiJzWhxtlvTkj+ZqsaYZ1OS0aLqjj89UXVxbxc0ASKevDzc5KfbFP7mbOVnVJT3LQ+qsZuHUHQhbLeutzRQCKZLqki5nxWkq4qwYIuFH2iWFPOA9cl1bivJ6QeHhLJL86pxcLjG1Uxmbbhj+PK11MTMFGCgbASOk2U1LHa1caqmelns0rgtrdDW+CrNfHskBKIHZ2VRE3BPT0rbUM5JVy9wJQ8PyzpD8PO1sXv96NZFYXbfs3OAZLfXGz67P55Y8elGfC1movreNQdD5+pcf++QT54f5ThS2naO8IcfGQrruvx9M8/xbYdIlE/1arDddd30DOQpFiusfm6Lly9aVuluMarLQiWvXfdpbMrwu23DyAR5LJlajWX1rYw6VSZdKpMLBEgFPLj1F38foPhoXRDxGNy7OMJbt7biyfh8DvD+AM6d9y5nrffurTwfj5TjfBb2lW3eOnnXniNb/E11zLmf3tSsi6ixv2GrjExkuO9d4a57vpOPvlonFrNRdcFAcskGguw47pO3n17iJGhDN95/DqCIZP33hlm6GIav19lwr//3gieJ5mfKxEIGPz1391MZ3eIYqHOzHRhoZDu6o6Sy1R55rcnmZsrLRSogYDBgYe3EAiadHaGuXQxw7kzc5RKNo99bxefF7WFji+ObHBgBaZhMDVVUIEKLUG6uqP4TI18ocbUZA7T1LHtOn39SYYupUjNl4lGlWDt+CcTDaeFEImkxfBwBsPQ6emNovv/PYq8JZ5cfybGMxWGZktoeLSGTQY7QvjWYkr/U+KnP/0p//2///cvfN03vji1bZvp6Wk6OzvX7CG+YszmbU5NNRXGAiE0NnaE6Ij6GSvCWLGpIIfZsmSipGyktidUUfLdjap4q3mKR3p7p1hR2T1Vlrw7Kbm1c3mn8nI4Tp1CPk8iEUdoxkIxdDbjUXGUAOly1DzJ2xOS9qBYZpk0WZJcyMHd3eqzLUIuCH7OZpUFU8yEB/oFcxV4cViZ5dc8OLROUHcVl3Rfn/L8e29KkqtJHh5Y+cb9L2c8/nqrtuz9HE/ZTP1+SDIYE/RHYLwIZzKSzXEo1FXH80oul+SX59XfhwzYnlSj3/em1LhcEzBaUKO8/ohS1jcToZpwpTLQl6htjBaUT+orY6qrulKaD6giqVByOT9aQC9f2UE7lVbpVusiio96aJ0gYl69C7sSSnXZMKBXav8mzqVrHBvJcHBrAkeYK7oMLMXRGUm2Jpdt48+BhlLON50k4jGLZGuIubkic7MlWttCJJMWf/zDRTKZCrfc2s8rL5xVNk+eh99nEEsE2P/wFuxQkHNZdd64Ul1PBwbEqouC5nuPThTIzBUxdY22jjC5bIVCqUYw6OPwO0OEgj56emMMbm7l5RfOUqs5BEM+hBBksxUe2r+ZI4dH2H1jNx99OI6Ukltu7efU59NkszaGITBNHb/f4PGndgLw619+Sq3mYnuSakNrGDIh2HiNaX3xWLXiSN6dUlGxezpNJsfzSAkz0wV6e+NcvJgil60Si/kJhnwc+3iCTKZCKGTy5Pd3MzqSYXKiQE9vFMeRfPD+MPWat2DtdeDQVtZtSGBXXV549tSyQnrdugS339VPrS6VUC2lAhNaW0OUKzVisQCnTk6zeWs7v/vNSQRw8NGtfKbF2NUi6Q5p6Cg/WNeBSkVF2PpMXXURdQ1D17Btl9R8mWymTCQaIB4PEAjoFIp1ZqcLmKZOW0cE0xQIDUoFG9Nncv7cPL3rk9jRMDVXJcttT4pVfU+XwvEkGbvJl2/cv0xWHP1LJH8Yl9zbvZiAV6wrq7+uEA3f6UVU6y6fDGcb4SSKg3tDf5ToSnybrwlrtcK/D661OP3L+ea/RrjuWlbv14H2qJ+QX6dS9xhJVShUXc5PF7k4W2JTZ5gdSR+5mrK96Q2rpKCNMTW6e2hJMeXTVNrRa2OSe3qu7HZ1BQWPrW9kmwdX5jpqgFsTZOclY0MzJJNBOjojXKhqjBbg7iX5555UhXKhpjLg2yxxhZfnR3OS+7oVx3M5BOuiShS1MabG3+Ml+N+nVQ78rZ2CiaLk0UaXdqzg0RZYTHa6rRP+9xlVFK/Es221IF2VS4op1cWM++HGVjUOr3twU7tgT7tgKA/nc6rrlwwo94AdSUFvRHU0H10PF7LKn/PoLDyxScWVvjisolkH49BqSd6bUnZRAxEl3hpscHp1IUgsuY8PxtW/H1u3+sOw2b17/vnTzBZcEn61naUdtM1x+OCs5K4u1TX9YEbZNLVb6hh0Wtfm6fjyqBKpbV/CBa44Srmc9Hn8/pLk4Iarb2M4r2zMlqZC/bloGpw3lc5jRcmsK4i2hEhlKnzy8QRDF1XqkuPBh0dG2X9wM7qhMTVdpK83St9AfKGY7wop3q5y4rn6fjbfe9NgAm8gxnO/O8XxY0rYI5HcdfcGalWXYr6AbTvMzBS47c6BFWkHszNFstkqwZCPfLZCNlPF86BcVoWeJgTRWID0fIm6K1Vh6iqucXSJ4K1WcxkaL7C5eQJdBZYheKhPLaIqtkepWMNxPPx+g3ffGWLr9nYuXZhneipPLlcFCaGwj4cPbSWdLtHeESEas6iU67S2Bbjnvo1MjOfw+03CER9tbSFMU+O3T3+2rDAFGB7OMLi1lfaOEH0DcaKxAPlclUq1TltbmGMfj9PZGaVUsCnkbUJBH9l0lZbeOK+MSm7t8NiW1FRqlHSJxVV6Qz5bpViyCYcD+ELKgL9/XYy2jhC5TIVUukwwaNLaGsayDPJ5m2q1RjAYpFSqYhgG+VwVv98gmy4TikWQSFoCkKspV5QvgivhrYYjh08TpKtKZLkS/jAmmavCi6MqCjlkqHuIUv1fef4FTJ29GxJcnC0xm7cBybGxPFs7VbPiLwVrtcJXh7XidA1fK0J+g5AfWkImZ6ZLzORrSE9QrDh0RPwLnElPStZFBK+NXa4kV0j6BY+sgxdGJLd0cEWH1K8L+sKqY7guuuxXC8XQSy+cply2MQx1WdSFzv5DW9nfv2jRM1ORvDKqeJiWobiXx+ZVlGoTHpJt8cu5lleiI6iKPcdT/LSiA6+PSTY3OLR1TyVfFR04PCW5vUs9zLclJKfTiifpyeXF9g2tKu7vJ9uufO+NMdXt1ITguSFFExiMq2K5XIfP0pCxJacykjfGoTesXrOrVf0znFe0getaBI9vFNQ8OD6vPELv7xV4Eny6UuwfnpYEDdUh2Z5UvNvoFxyPJuyKy8svnMGtu5gaZGsQMiTU1M8ff2onhqWK97cnJXd0CTZENSqO5FJe0T3eGJO0BFSyFaiCe0cLy86bfINP60l1XrQGJH5NLRgCOqy3YDyv6BpL0VwYFGoqHrfkSA4MrBzz+OdA0zSMhsl5pyV5c8JlS1Dj2PFpvLqLEOp3uoCZmRKvvXaBR769g3zJprU3wWhNQE0J6TqDK0fxXg1SSnRTcNe9GxZG7QLBpycm2XdwM++9M0ylXKdWc3nu96doawux7+BmDr83wtDF9MJ2YjE/l87XcFyPeDLA6bM1pGQhiz6fq1Is1SmXakgkFVcd86XHs+xIZZCvqejfL4bgzm6hxu0tQX7/65PcfucAN+/tpViwufPuDeTzVQp5m2RLEM/zqNoOk5P5BUX/1q1tgDo/YnGLUNhHImERsAzm50tXFKZNnDo5QyDQS7Vax7IMki1BJiZyzM2U6OyKEvAbvP76eZCgG4J40qKK5O4ueHsKgobHQFRTgjAAoRGN+bFCJk7dxXEkc7NFXM8jmQySSAYVlSJgks1W8PvV6N6puaRSZWJxi2LR5v33hrl5bz/BkElvFKRcafG8Ovy64I5OdY862KBeBg11/k8W1aImoKuwFMuAJzcJhFCL1nQVdraunNA2XZa0B8HQNbZ0RWiL+hlNVShU6pyZKiFQjYw1fLOwVpyu4S8CQggGO0KqOF3hhqkJQdmVFOqsamViGYL9/fDrC5LH1klarOUj1pvbQCz5UdVV3cpKxeP5509Tsx1cCcKTlB2Bpqli6Cc/3rUwTvxoRrK/fzHF6N0plRu/bF8RXEODBxpK/aYANGCort3OBm/1oxnVfQA1Hv/NRbivB05nVAfsYk5FSO7rXyy6LEMViCshZC7at7RYcGRa0heGG9sEMT/LhFZVV43l/uWMioHUBDzQJ3hqUPBpCsZLMBAR3NapxnXvT6vx/eMbBT/ZplF2VLrV88OSYw3h8uY47Om4urOCEIKZ6cJCIRQ01XvrSzpoM9MF+jYk2NXqcjqjRFH7+kAKFnxgb+kQfJryKNbUMRFC8vR51bndEFWCtI9nlXl7MxY233ht2FDF9JFZIAB1CT4Up/dUWnWfb+2ANyaUt+6G6LWP8l1PfqHISgfKJYeJsRxzDa5hd0+M+zsN5hyDhx7eypuvnIVogGK+iofEChjsP7iFUyenuOW2AUpCQxeqSz7esCW7rRNavoCecDmaGeqPP7WTmekC6XRFdRpDPh7YN0guW8F1JJ7nEQiYvH94hOFLi4VpIGAQDvnJ5arE4hbBoI9i3laJv1Itljwp8Zk6gZYgtqs8gMVCZaZ8ez0J3W3XEv27CE3AzHiOTYMt3P/gJp7+xQmsoEl/f5xSuYZT97jjrvV89ukUF87P8+i3tpOIWTz/7Gls2+GzE9Pccdd6BFIVpQGdUNiHFTI4v4oaH8C2XaYn80RjAUAjm61gaBqRtgCO4/Laq+eZnyuh64JQyEdHR5hLacmmGNzSAX+YgIc0j5qn0e7a+Pwa8XiAkNBwXMV7NX06LbEgSEilSkpEVa3jOErZr2kaEpdYwqKYt/njGxe5eW8/Z05Pc/CRbV86lreJrpAgV1MFqidhvgKfp2FDVOA3IFWFsA82xxYnWyEDQuGVt3cpL7mYk9wXEAsCs2TIR8wyOTaSpVKDU1MlLJ/+tSVDreHrwdq3vYa/GCzNcF7p5ulIGnZKqz9gwyZsjML7M/DIuuW/W1oUeFLy0SyU6tBVKpCvqAhF6YIt1cMwYkC2ulgMSSm5rUvwh3HJA73KkD5nS3Ykv9wDv4mmynep2MfUVDqTocHmhOBCXnE+b+sU/OqCEgiBUib/cULSbsG7k0q8syWhLKVqrqTqyquqxjsswZ1d8OyQREpIBJS59u5WtRAI6IoqMZpXQipNqBF43WsITTTVzQoaggf7VI/rn09LXhqVmEKyOQE7khoP9SuPz74w/O8zknNZZfTfbq1CrxAsZrED5gqehtlMhQGR4MY2DYnk03nJ2awKIqh7EPFJHuoV7GzR2NkCowW5MPY/n1X+rPmaCj6Yr6gO9uVd3Y+mBKYJO/o1DENwKa9cBjqCagx6Mi359npB8EtwXEuO4j7v71/9b3RgerLE0z8/TqlUWxDUWJbJk9/fRTgWYNgX5Knv7+TCaIFavkI04iMWC+BKj/0HNi9Rti+lKsDr4xJTkxzo/3Ids+aYv39jkkAgz5t/uESpqCr6UNjH/Q9tIp+1ee73n1OruQujbL9fKcZPnJjEHzB4+JGtvPbKORqNSTyptm01LI9i8QA+n45ne0gpqddc6o5E0wWJsO+aTfkX9rsxri8UbCJRHz/+yU3MzZbIZits2d5OPG4xP1tk67Z2Nmxs4eiHYxQLNfYf3Eyl6uB5knrNYcPGJNG4D0cuRnUmVohXbqJcruEPmDz3zCna2kPc98AmagGDF587w/y84tgbhkZrW4iHH91GSurkah75mko2A4/XxuDGdkHSczFNjWxGJUAFLJN4PIDPr6NpGpVyjWgsgGUZVMoOPlNHSkmt5qAJjYmpHMWCze4bezj52RR33bOBYqFGS8DiTx1QNxfjx+clngff2iAWfJq/DD5LSbI1uHeFaGFdE/QkLM7PFBHAXKG2Vpx+w/CN/7ZN06SrqwvTvPb87DX8xyDo0wn7dYq2y2S2Sjhg0BkLLPxe19R46Go4mVKjptzKEeML0IQqzmpScOaTCmFDGZlIQydtC5KBxSSdZjHkSkUfONAPfxiX3NKpisS0zTJe5RdhqdhlqV2O69OX8AKhJQD39wheHZMYmmB7QnXtrm9R3YsWS3UjDU0JpUKGEiYd6Bf84pzkrzYoo/pmB8P1JEKDz+bViL8lIHhyEN6ZlHyaUkXoq2Oqw+w3GnYybYv7/cQmQdZWtIb5CmRssAyl/ga4PgltQRWa8PKoJOmX9C7hl/1km2C2Inl7UlKowaPraCjIF+FJSTRh4Uq56gMvnljsoN3Upjxc7+1RI8TJkqIoPD8s2ZqEG9uWJzYV68rB4MZ2QcIvVv3ebuo0qddbGC1qfJr2aLEE+/sFr4xKvjf45YrSJkKGEm2dzcpVfXbLJYenf3FiWWEKShjz9C+O89d/dzM9hobP0tm2Jc7R2Tj+kKA9srjIWUnRbhkqc15t809cTCGZmiwsFKYApWKNt/5wkXvu3cj+g1solWvkczbdPVHi8QBzcyW2bG3j4KFtlEv2gjm8Cp4AX8DgoYNbyFY9xos69x/Yyh9eOE1qvqw+i4Rg0OCW2wawq841G/PDoi3X66+c59Y7+gkETCJRH/6Agc+nzO1jgy2MjGSJRPzsvqFHpUMFDDp6oiDB79MYH89hpcxFQd5ldl9L4TjegmDN59O5eCFNIX+K++7fwGPf2c78XIlCwaa9M0JLa5CU6Wc0q2zY8nX1vWxPavg1j5oQpKYqeG6ZM2dm+e4T12MYyme2Zru4rkMsFqBWc3jud6cJWAbRmEUi5scTsG59knDYpFKu4boeD+3fgvRcfvvbUzz1g10M1XQ6g9ceD7wUhqass+5dv3phOlORxE3wr3CtnM9KZiuKNtQ8H9O2JFtddFVJl2poDUeA9qjvim181VirFb5afOOLU03TsKzVV8Fr+GqxoyfMB5dyaHicny6SCPkWMpYLNWX4fjVsT4CuaTjXOP/za5BsdEEEYoFvJmgUdULijywfJ4ZNwc3typbp1g5xTWKCJi63CVr4ualx+0Nbub0ruODp6Xrw6pjkqUHlSHB8XpL0w8F1Kpr0zi7BO1OSH26GY3MwXFC+ps1El99eUjutCSV8mFtsSCKlYHebKpj29Smxw4Ws5ERKCccAtiaUsv5iTv3NjqQqmJ8cVPzKtC0JGYqLdikvOZmW7GpT3MZbOlSBuqdD2U5JVIe23VJ81U/mVJH4xCYluAr5VNDAyRTc2RrGRsetu4Qvi6NdKdbyuhbBp/PKiL2n8dmTATXK/vUF+M6GxS5t2BT81QZFx7guKVbsXoNaBL0wYhD3w13dgpdGVOrUPT3LC1MPtc/JAMsK8UJdEjaaiUSLuKUTfn5u5eJU0zQmxnNUyssL0yYqFYfZ6SLxFgs9GcGVkpvbJa+MegiUUO1qvNfLedpfFt4KHUMpJUOXMmjiEvc/OMjERA7LMtENZf8VDvuIJ4JUqzU+PjrOwUNbKRZs0pkqiUSASMTP2bOzbLtjkE/mPA51+7nz7nXk8za5bJVk4zWfn5zmwyO1ZYr9kiO/8DP5LZ1bbx/gyHsjbN7ShhU0GmECEtdzcW2JrmvksipmeP2GBK7nUqtJ/vjaBTKZxYtmqSDPb6n/XnodSymxbYd9BzZz/JMJDj26nad/cYLZ2SJP//JT4gmL7t4oe/cqx4JAW5RsVXJ3N9iuWDgnATbGNfJ1yJk64aSfubeKPPf7zzlwaDOGbpBssajZLrZdJ52qcMfd6ygVaxQLNoGgj9b2EJZlkE6V0XWNvv44uVwZT6ou5dhoDqO7lcPTLg8PwJdZsJzLSobykscHxarTmRPzHmczcH2LYFty+e/mK5LP05JvbVgsTAt1yRtjypWkCaPhadoWMQn7v/5SZa1W+Grx9X/jXzMcxyGfzxONRheEMGv4+mCZOjt7w3w6rmIDl4ofdI0rVPGXozm6/6L0oSaWmp7bNQfP8zA0jbqnojLbwzqhljBDOW+Zj2pnSHlhFuuq4LlWNMU+SwvTiiup2C6HXzvDU9/fhRruwkRZWTYNFxQ/1K+rmFCfJqh7SkTQGlCFliuVXZRS3cNPtmnUPdWhHM5Lpsrw2HpBzKde97tLkqhfccW0RoG2NQnjJcUd3agaRxybU0lQAnh9TO1v2YEnNrHMa3VDVLBhyfHZnlS825dGJOcykqKjiu2fbAMQBBq82NdGJYkAOFJganBwnSBmGjz1V9v47TOnSVdcWhrF/2qxlrtb4WfnJDVPsrdDLUySAdjXr/HPpz2GCjC4JKjBrwv8GuSLLoX54kL3urUjzMc5jeG8pMXn0qaVKTpBXhnVub1LsK5hweUh0RBomsaZDIT90Bv2Fm0fUbznVouF+FnZ+OVQrpl4I68oJHUNUnPlFQvTJlKpMlbQxC47+CwdEDzQi+pmj8K9PVcPWfhzcHlAAALqNQ/P9ThzZg7Tp3PvfRsZG8uRTpep2S4+v85bb17gvgc2cesd63jphdM4mo4/YJKaL1CveRw4tJUxzeDGNsnkZJ63Xr9ET9KHNE2GJgro9doCB3V2pkjvujjPDLn4dXiojxUs0BbR5Mw+uH+QmekC+YKNq+lYiRCzno6l2MnVVgABAABJREFUQzwhmZ0pcu7cHBfOpzh4aCuvv3KeYMjENJU1l6Zp1JYI8kxLX8bFzWaqBIMmUsLnJ6cXqCk//slNDF1Kk81USCYtItEAH300xvabBxA+nd1xtZ+mrjyE5ytyYZoQNT18HWFScyUefnQbLz53mmd+c4pDj21TI/2KQypVIhYL0NIaxHU8CvkqpbLD+GgW1/GwQiZ9/XE+PjrGjuu7yKTLFAo26VSJu65vY1Psy1mfDeclp9IqVnk17vRUSfJZCh7sFXQuU+ZLLuXgw1kVN730/M/X1AIvVVWLju6QwDI1JIJ0qU6+4nzttlJrtcJXi2/8EXZdl2w2SygUWjvh/kJgO4tP56W3to4lI+CVHu5/KppdEKXWd7B0ge0KwgH180jC4HcXPWYqcH0LBA3lEXlrJ43i9NreZ6nYp/kZinXFs0z4wat7y/itH88oAZjtSm5oVa4Aza6oX1fdvYqjuhMXc5LvbhJICf9yRvLBjMfGqDJej/kgP6WK6Yf6lRfsljjMVpQ4aGH/ENzbA8N5SNkwXpRsT4hlNksSSc5WyUpTUtIVUjQKTQg8JPYSg3dTU1ZUpboSTvxxYtHVYHsSBuMaUgh+f9FD1+DmdvWgbBYUf/2jXZweKeCVKrReJSnI1AQ/GISnL0g+T6vftlrwYK/i6o4W5DJfRQ3YblR59penKZWUzZBpaFghHw8c2sa8GWCm5BGpl9nZa2GY6vgemVad+5KrMWA4TI3nyM2qFKZyT4xgyEAzBBW3kfIVEzgNx4Wjsw2Zn4AOC14dlayPqo61lCooojMEg61BrkbfS7YEQUChYBPGj2np+DTBo+sE+ZqyBPuPKk6B5R3DugoIkEDAb7BhYwuvv36eHds7iIT9pOZLJJJB7rp7A8F4gE/nJbc+shMvV6SarxBPqO80LXXOT3k8PqhzbFgVdaViHahTdRSvMWxIHMdlYiJPVejcGAvQm7y2+/VSW66qC8WaoC0EsbqkVqrzP//nR9RsxQOKRPyUy3UqlTq27dDaFsJrFKewKMjrWR9nuCDpjWj0bUiwXhecPDbNkcOjC+87PJQhl6uy+4YeolG1XcNvcP1dgySixsJ9o+5JXhtV94KP5yQ3tS3SXcJhA7vqQ0rJ49/byexMkbffHGJgfYLBzW1EYz7mZkuMDGewLB/T03mClo/unhilkk08ZvHx0THa2iMEgwbTkw75XJVE3KJScjD82jXTJDypLNYODKxemAKczUg2xlhWmM5XJO/PSNotwV9tFFd4IY8X1X0lv2Ty05OwGEtXcD3BibE8t26Mf60JUWu1wleLtSO8hr84NMerEhVltxRlRzKSlxybg7hfEjIFVVdSdZWtU9NDr1iXhE1VHHxRF7VZDH33ezuZHM+SztUIxYJs7Fsshr69QTBagDcnlEXS7V1fJj1IYanYp1mY+nTwa6qwKzuS8dkKuXickZxHsa4MqzfGBB/OSB5fclM/0K+CCPI1VYgBFGyI+QWPb1Kdu1NpSX9EckObYFNU2dSczypF+6a46sbma8vFQKamOp6DAB2LP/ekohWMFtTnCJlKfPTxHA3OqfrOBiJwf+/i3wkEYRPCpiQ0o7wO+0ICu+ItcG73JiykESZbMwgb6vtqFhRGe4y5apzruxRHcbUHadlR+7OnHTqDiqf7/LDk4IDg/enlr61VXJ7//efMTBfxlnTmc7kqrz//OT/40Q04mmB0RjBclAR8SqX8UJ9yDZieLPE/G4KlZre0KViKJwKcrJrc2K7Ee09fkGxLKJeDuqdG43G/IGNLTqfB0CSfp1WHNRmQ1MsxLMukuITX2YRlGbS1h0BCJlOlWnXoHogtXC9NsdxbEx67WlcOpPhz0bxWnnhqJ1OTBWZni2iawO83+PDIKHOzJc6dniMc9RMO+bgpaVGu1Pl43GNLAnpDOqI9gSYSeBJc6fHGOY/vbhQgobVFUWiai8+QIajVPeZTVWVtZei88fpFTOny7ce2XXNqFKh5hFeukx7LcW6+xMBAnEq5jl1Rx1oIgRXykcuqa1QJolz0yyYj6XSFoUCcqKkS0qQmcVwlvrosb4NMusIf37hAKGxy2z0bmfSFuS6mphGgrNvem5JYujqVdLG8QHWA1laLStXFcz16+2J0dUeZmSly9INROrujRMI+7IpDZ1cEvz+B40qcuoNlmYyMZGhtC9PeEaJYsDn64RhW0Edre5jRkSwdnWFCUd+KxzBjQ8inpgyu53E+p3juVwu6mK8oo/6NAXWtd1gq7vi9aSXEWyl048S8cvD49oblXXBdE2zpinB6Io8rJe9fzHLLhvgCzWsN/7mxVpyu4S8OdVc9bAVwaa7Ehja1lJ4qKXuj61sFd3WrjoNlqhtbxKeM4XvCkLVV7Kahqe1sS8iGCnZ1eIDuk0STklKyA9PUMa3FYsjUBBtjqlAcLUh+dlZyT/eXi0Qt1SWJpIUjlYjIp6nCdOFzexCOWfg0yd5OQWtA/eynZ9UYbalv6nhJFTmTJTUO3BQXTFeUmXbEVLzZm9tVQfTMJYlPU3Gl709LBmOqsF4fVTSAmE/yyPrl3YyJkuJMxvyCqZLk5VEVGZux1feyt0PQGVSv96QEoTqSq3PXBI+sh09mwSyUeeOls0uoDZKCpzpyh6VFtqa6vs1R/HzF41/OKJuniqOK4w0xQae1aPYf9wsODihebasleHKTspiaKatz4NcXPQ70CyI+jcnJwhWFKahiZGaqyORkgd51YWJ+2BUUfJiSXMwp/9Tb4y5P//wE5SWFKSwXLG0IaFhBg1+ec/nB5sUH8lK2WsIvuKVT8s6EWmg0O2WhsMmTP9jNr352fCGRCFRh+sRTu6jZdfyWyfRknta2ELomcBrXS3dI8LfbIGcrqsctHYtpPv+eEEA2W+XI4RH23NrPB++PMDqcRQhwG/uSy1Sxqw66phGKBGhxFzm5TcU7wKfzKvHN3/BzbWtSbFyPgAF4kmK+CkgCAYNY1I/fqQFi2Yj9atAAz5FMzhR5+ucnqNpKwZfe0U4iYQENFb7rUS7WiMethQ/qOB6+y/iOkbhFzVgeVSykpK09DFJSLjsL6VeiMVFIl138EYubY4sTn6mS5NOU4o6/Nia5r0fw0awkZC4vUOuAL6DjOQLpQcAyiCcCZNJlJsfzeI5HZ7cagYTCPlzHY26uRiZdIRjy0d4epmrXef7ZMxSLNt/7/i4+PDJCS0tIicNMHX3JMWwKNmfHCsylVIc72hZmtKyzp/3K80kiOZtRmoDZiuSGNvBQThifzqtidf8qhSmorun9vSsl1EFr2EdHLMBMroonPT4bL7CzN7IWbfoNwFpxuoa/OHTH/UxlbaqOx0S6gmXqdMUDxP3w+KalN7nlN7OH18FYATbFlBK7ifenJc8MedyypKBaDbMll3M1h+8Maldsv4n+iOBvtikO5bWg5KiR1YUc9IbDVNExNXfhc3gobmgypLNlIIK5pKD4PCXZlhSXqdolZzPqYTaUVyP+sYLkprYlxSKwo0V5rmZtZTY/EFFcT9mYrl/forir57Lw7CXF2Yz5VM01V1GfPuKTaCi7mCbH1HYlr4xJUhX1Pn+77doM6IOGYEvI4bl/O4O+7OAJgsLl5RfP8KMf7kIPaEwU4ZkhyfaE4qMNRODeXhXnWnKg5qpEsLTNQiZ5Z1DQ2chLODaviv8NMcGGmBKvfTIHD/RrzM4UFgtTVccvwPMkczNFAl1Rzmcl5+ck17Up/txcVWNqPEOlcnXBUjTmJz/j8b2+MEHduMKyp+6pGMeLOTX+7FjMeMCRko7OEH/3D3uZnMyRmi+TTAbp7I5QzFexbZdKxcHvNzB9OhdykK9KdiRVp0kgiPvV8To8JXmw/88XQl2Ocsnh+d+fYm6uRLFY466712NXHKanCwuvUVnyW/EHTV574yK3PzDISo8by1j0mQWY9XQe/9ZWXnzmc6plRwUNeBJ/QNlSnTo5TZP3UKsvt3lbCU0BoiYEv/zpcSrVOromcF1Jer7Mpk2tuK6HYWh4njL6DwZNldBkOxiGigrVtMWo1d7uCONZydmsiu5tvseRwyPccdd6Xn7xLOWys5B+5fkMvv3YVtpjagojUZzM+YoqykxN8YaPzqoF5UoFKgCeR6HgMDdbRjeVR2qiJUg+V8W26wQCBvOzJcrlOsmWIC0tQUyfzvRkgZGRDLffuY7evhifnZhiZChLb1+cdLqMBPoHYjhSLhNsFqsOjlTXkc+nc/tDW0n6g3goLcB0w5Ltg2nJhpjg+ha4uWP5vaAzBCP51f11Z8vKDu9qvP3NnWFawj7OThcp2g6fjhfY1Rf5Wkf8a/iPxze+ONV1nUgkgq5fffW9hq8Opq5x07oox0bzlGseF2aKmIZGa/jqdiK+RnfzctzWKcjX4M1JiaWrlXzAgOsSYlnnU9M0Rqp+Htlk4jN0XA88b+UKVAlirrY3kqorMHWl9O4JqRv6SE3ne9/Zymsvqs6hhxqtJ0M6jzyy7QpO5faWK7d8ZEaNcCuu6iQemVFq/LGiGtlrQrA1Ac8NqeInaCpP1IcN6LisON/ZouJSN8QEI3nJ+pgSOrWphhJTJegKLh+3+XXBY+tURyhbvbpCfOnx+HQejFQRzXW5vPDXhSCMy+h4gfFQjH29yhZK0wRPDcIvz6vuzLaEwN+4VH+4BX5/UfKHumxQCRa3GTahPbi4bxuicHTGo+ZJIpHL7BWaf9aob8IRP3lHKXP3dekcnoW5qmRPp2BmvvSFgiV/wODYJxNMjed54ge76O4J4yy4Aaju/4ao4IaNYuWIVU3x2/x+g/7+OPl8leGLaQxTx7IMDr87wk17eolE/GiVMlUzwO+HPDbGlDgMBNe3CLpD8PaECij4IuP/lbCSk4GmaUyO5ylX6nieEhJ98vE4j3xrO6lUmUy6TCTqp7UtRLXi8MknE+RSZeqZIiK5WEQ2t112WHDWqLhgp8ocPjLC3fdsoFqtU8jbmKZONBbg+MkZCukyrqNSkgxDkE5XWL8xgcPKrgt2xeWjD8cYWJegUq2rtUjj15MTedraQgQCBvW6ixAN/u+RUfY/vIVXXz6HaWoqeEAo6sahx7YTsHTusCQvDku6gxBwvQWRY73uLjgSZLNVookA27a1EwwbeKiQjXenlOBn6TnbFtJ5cEBwct7j5nZvWYF6T4+gkqqQSZd59nenCAZNENDVHWXnri7a2lVkqcQjkbQwTA3dEFQrDkcOj1Is2OiGoKs7iqbBW3+8RHdvVHHgZ4r4fQbSA9PQKBfrC5/Fpwmad9xy1eXNV87Q/8Nd6JbGs0OS3ojisz++SaxaXHZYgg5r9QtGIijUV/79UkpWS9jHzt4ox0ayFG2X8zNltnev4uz/H4S1WuGrxTe+ODUMg7a2ti9+4Rq+Upi6xu6+KEeHc9RdyamJPHdvaV3xtRlbcSGXjtkuR9QneGydEgHF/eohdmwOPpz1cLyGeX9c57bOCOPnM6TmS7S0hujpU0KXL2NYrcZiHpdGVaLOjhaLuD/MtGGwq0ViiOASlW+FSNyiu2tlsc/laUqFmoou/d6gUJ3YEEyU4JEBZTvleJIdSY3bOgXns2q8+5Nt6uHx4ojkyU0qKaoJQxP0hJWHZdgHR6bhpnZFG9CBgejqamgN5Qf7xZB8MNOIzyxWVi1mBQKtUmFLb4J/PeuyI6kiQi1D4/FNiraxLSHwpErG0lDiihPzkn8+LWm3JJvigi1xwbaE+mcpdA2mikpx7g8Y2FVHFaRLXhYIGHR0hmlNaohkEteDJ6IeZzOSSzmPltbQFwuW1EemUqnzq58f5yf/ZQ+ZTJmOzgjDtk7HEjrCSvCAeDKApmuMDmcoler4fTqeJ3nvnWFu3tNLIGhiVx3efvMcjz+1k60bNU7Mw1sTcHePGh23BEQjZvJLcqNZ2Yc3GDSQnmR+voS7pPNdqTgc+2SCc2fmaO8I4w8YfHR0nEq5jj9gYAhBIVtBEwmkXNz2xGyFaNxie38EDZ1a2eXw62fIllzezZRoaw+xeUsbr712gUqpRjweIJWuLnhfIkB6MDdboVarMzNVXNhXv6UjGwLEYMhHqmF+fzneeesST35/F7/+1adUKg4gmZstceyTcf7u7/cwN1dkciJPJOInHPHz0YdjPLh/ENPS2RiDXE2Qm1sUOWbSFd55+xLC7yMYNJmdK9DWGqQvkmC+7PHhrGRvu5qECKGoNPlcjfGxHPNzyh810RvjoX6DcznBdXFBvWTz5hsX6OtPUK+7SEyy6QqZdIVTJ2fYe2sfO67rJJMu0zcQw+c3mBzPk05VsG2X1rYw4YiPcNjHkfdG6OgKc+99G3n15XPsvrEbf8CgUqmDhJnp4qqxrOWqy9hEATsZo+Sw4oh/Zaz+uos5iK/Qc3A8yU/PStosyU3tauIVDhisawsxPFditlAjnq3SHb+mG9C/C9Zqha8W3/ji1PM8HMfBMIwFReYa/jLgMzQ2d4T4fLKIQHBhtsSm9iuNTucqKm3kasVpE0tFTLd1qq4qSCqOoDhb4n//7Dg121koQKyAyRPf30Vnd2ihQPWk5HwOJouSgKEiPJs34KVjsbLtLKjxQwGd7zy2DUNYy9TDAw1hyGrm6UvhSckLI4qjlqrCqbQkbKru57F5yXVJxeFLVSV3dwuifhrFo+C6JJTrylJq02Xd5aABc1XB3d2CTTGVtCQEjBRV9OhPtn3xcZULobNXvrbqqpSmJwc1xopX9wmMJyz6opDYIDg2Kwk2Cmm/ro7jsTmPc1n4zkZl9g9qUbKrVXHefn1RqeB92pUepkEDIoaHFfHx8CNbefG5M9gN/iEoLt+BQ1sJhX2cOznH/HyJ1sYCZUfCoFJxcQLRLxQsOY7H7GwRx/VwCjUmxnMc+3yOVKbKoUe3sqUn+IXftQtEYz42bGwhlSozN1PEMGDfgc2UKnWkEBw9OnZZnKviML47KbmjWxXvTd6CJ69uudTEaj68mzYl2bGzC6fukkxa6EvuleVSjVjMIp9XJvvRqB9NE+hLeIHxhKWiNpdsO2Ur397jPoNDj22nmK8g6x6xBrd6fLrI+s3tODUX6UlsW3U3m3VxoJEs9S//6xMOHtrCJx9NAIuWY+2dQTLpCrlshU2DKy9sz5yeA+Dv/mEvE+OKRtHWHqKlNcSbf7xAe0cEocG583OUG4qz5vHuDUtOZSC4JNHMkSq21/Jq0HAAyGYqmOUEn84r03lLFzg1D8/zmElVFd2gspgaYlkm3//RDexI+illlF2UJyW5XAXT1HCc5WfP+FiOvbf2k06X+e2/fc63vrONzq4ohqETCpmEwn7a2kNkM2XWb2zlhpt7+c3Tn5HPVYnFAsTjFuPjOWLRADMzBep1F9PUcaXEkcpRwvHUcU9lKrR1xOkOKUeQvR3XRulZDQEDXO/yv1cuJT1hlSS3lIrVl7SYyVWp1iTTOfsrLU7XaoWvFt/4I1yv1xkfH6de/4JIoTV8LWiL+GgNmwi8BilekrElhVpzFKTG9B6qALoa0ja8Murx7qTH8TmV6Vx1G62zqsMvf36carW+lIJIpVrn3355gnJJFTFzFckzQxLbVZzNm9thaUG21MdUFxBrCHi9uvq5XVl84CsjcImUkkt5ZUx9Kq3sWs5nJRVn+ef5NAUbowLLgPemVGzqwQFBsa64b9e3ClwJ6aqK8zSEitpsbme2ogr5kYJc8N0EFXs6W1Z/0x0S3NwuuKlN8MiAoN2CV0c9RvKS+YokX5N4Ui7wWgGG8spQf7TAsu02oQtFQ1jqk7kSlhrsJ/2CB/q0xvFVxWaxrrimGivxfZVg7L4ewWtjSpE/dinD8aOTjF3KUK+4tFka4yXwB3TaO8I8/MhWHtg3yM17+njgoUEOPLyVjo4wb7x6nt/95iRvv3mJ3/3mJL//9UmmJkr8+pef8uGRUZ74/i5CoeXtnqWCpXLRJtvIoAc16u9ps/Dj8tYrZ5jNObw96VFcZZzZhAuEoibBoEk8aSEFqmtZd3nnyDjD0xUcqXiSzQb7zhaVdPbaqOTDGRXb+vywJGtf9a0WsJIPr+t6bL++i+mpAs8/c5qOzggBy0BrvGk+bxMMKZ6mEKDrGkITGIYGcvF7rZadZdtu0n1rNZcjh4dJpRaLvLqUlOtw/tQUBw9twefTcR0PvUGDCAYNHn54C0feG6Fu1ykVbKWWb2zv5RfOUK24JJIWc7Ml2tvDWAHlQ3p5jT4ynMGuOnx4ZJRSqUYgYPA//+lDzp2dJ+A3GBtNL0vFah7viCmwNLCiasFle6owDZvLJx7huMUnc95CYZrP2AxdzFCz5RWFKaiO+y9+dgzXlRSLNplUhUJeLQCAhWPQhOcpdX5bW4g9t/TxwrNnyKTLhEIGHV0ROrtCRCJ+unqijIyk+af/9wfkc1UOPbaN1tYggYBBeq7C7EyRRCJILlvF8yT5uhJkCtQCNu6HrlaL9VHB/b0acxU1aflzYLtQbVzLISqUHMmzQ5KTaRXesVLDoXnV+M2vtnxZqxW+WnzjO6dr+MtGM8IQFE8tX1PG8HVPFV2mBm2WsnR5Z1Ly1ODq/LqkXxDzSearKkt+ugynMqr7ua6Qw646StkrF2+AEiiX60yM5Qj0t/LepMehdSvHV17uYyoQOI39NzSWdbkuF3DE/Y2CS6jI0VxNjeRtVxWMST/ka5K4Xyl7nxoUC5Yut3cucrP6I2qvj0xLfrxV4HrKk3RdVBUux+eVU8B7U/D4RvDpqvNxcABeGFZK/maHTdcEh9Yp39PzOcWNzdcXOXvrIhAwlI8nqEhXCfzttuU81MkS7Gh4pa6UrAOrG+ybS75LU4NbOgV9ERZ4p5djQ1Tg5sr84udnVLtnyfYffHgLh7MWpibZ0mZhBU1mp4tYQR/hiFI1v/WHC3z22QxLT6Gdu7v4xU+PEYn6GR7KEI74lgmWWlqCdHQpwVLVdnnrzUvLhFYtLUGOn5ojVwPTcUnNFBFhNRr9Io9cV0KxaHP0gzEsy6RSqVMq1qi6kqCpOJtL41wB7u7WKDmSuqsWBlbbYnZ5xZEcnlaj5aXuD3Dl+Qvq+mtpCZLJVCgWaszNlcjnqxxqmMLPz5fxPMkH749y8NAWDr83gut4aHpjbN34XgNBg5FLmYVt265EE83uripwNw36cCRUXYknVfJaJl3l1KkZ9h3cghAwNV0kFvUTifg5fmyC2VkV1pHPVbEsc6GIrNVcpibzdHSpxdCJ45M8+f1d/OoXJ7CrdXRdiaKsoMkT39vJO29dIjVf5ua9fRx+b1hFl1oG4YifYrG2zNcyngwihIrL3dnqMamHqaLhuC5R3/JzXzc1tFiYW8LqurKrLnOzJc6cmUUiryhMm6iU66RSZTxXEo74qZRrBEMmmqZhGNoCPxZU53pqqkAiYdHTG6W9Yyvzc0VyeY/Orgia0CmVbaanigSDPh751jY6OqOKpiElw0MpgiETECSSFv6AQb3mkrCMZWLBy9PZHuoTnM1e/fy9GkaLklRV8vCAwOeUKHz8NJPrn+DOrhDJVQRUtuNRrbkI5Jog6j851orTNfxFY2i+QrpUR6JhmRpxv2B7UnXQ3hiXJPzqJh0xBSlN8vq4ZGuCxhhT/S7qV/ZUxbpgtgL3dAtiC2p+gWloHH6zhGiMgGHx301K4uxcmY5e1alcLVd9qY/pUgSNxQdWNlNhQCSU+fqSIIGkXxWgTXR7krytjNWH8pLPatDk/1vGcq/BpT6uF3PKTzBXU4b4PSFV6MJixvxwXjJSkPzsnFwoJIOGYDAm+d0lyXc2LhYNIFgXZVk6Fiju68m0UuBvj0ssU9AfUSP8V0cl5bqkJyzY26Hy60+l1ZFs+mQu5dw2zdi/KDe97imO2sar2HfZFZe3XzlDpqyKoEiji1Wrubz+4lm+872d/HpMsCkm8IcMYv0JMkXY0CY489kcn302s2x7kaifcqlOparU0H7L4OSnM0yM57l5bx/RDQEcx2N0OANCcPTDMeZmSgsnjmWZtLaHmXtrmLCpOkUzqQrBRBz1aa8+Em12m+s1d1n3TsVGSmpCp21JwTBTkUyXYL6qaBYVR3VgkZLuBj/5ts4rC1NQi5FMQ/zT3H+n7hGNBSiVbPI5dW6/8uI5Djy8mb96YieZTJn5+TKRiJ+evijfX7+b+bkS2ezy75XLrg1HsqygLhVrhGMBTEOjZtfQPKlSw0ydqdkK6dQw9z0wyMR8hbmZAqVSjXJpsbCLxQILhWoT2UyFdRsTC4uhqif565/cxNysSgVrbQ/R2RFhdq5A30CCwS1tHDkyil1xMH06Bw5u4fOTU8u2aZoa0YifYx9OLPBbW2MG3/7WVv6wzB5NFXO3PbSFZNQgakLd9pieUOd8V1eU1Fz5qt/93EyJru4IVtAkYJl8eGSUhw5s5oP3R4jFAuTzNp4nqdVc2jsivPrSOW7a04thCMIRP/6AQSTsR9NhZqpMtVJnw8Yk4ZAfoYGmC0aH0zgOHP9kQlFGijb7DmxWCyxUN9ingd9nXLF49OuCnSsINq8Vl3KKoqQJAekhxt/4Z7b+lz24getW/RufLvCZOrU65MprHcz/zFgrTtfwF43ZgnogS2CwM8yptBpVxv0qIvOWJUbxezuUuflbE6or43pQcMBoPGy3JiUP9YkrTKRdD1paQwvv0xz7LdR8EtrbgmyMgncFP2oRK2WPG0Isqz9Ul0tyOiM5MS/pC6tulqEJbFcS86sYz+G8ZG+H4PauRUWvJyWvjUkeW7/yPvzzaY/+iDKMvxlVvHcGlxTcSM6m4dPUoo/sqTTsaGRf725THbfXRiUP9S8tUK9ExCe4tVPy0ohkpqwO0seKvkdXCHa0CN6bkmyJK6/UQk2StVXn90/l3D62XvDcsNpOMiBotZqergrNzp/meiQborelI9xazWV+poBPj1FxJJYhiJoe18VBQyc9r4rKpT3tUHDRlN1xPAKo32fSFV57+RyhsI977t9AZ3eMn//rMWrV+iJf2TJ58qldfHZ8Ag2VlOXXYUOnRdGUvD4O316vOvdXw+rdZoMnDm7hD7MaMZ9Hylac6u4QbEuqrnlgYWEkeX9KEjKuDI9oCqCm5/MEAgY128E0dZX65Unm58sMrE9ix9R7z84UefnFc9xyWz+6JkgmLUIhEwEU8lX6BuIMbFr+vYrLrg0hVFe3ibIjqdcc7rhzHS8+d5paTdFoJIL29hCPPLadD94fYWqmREtAfddG47j5A6rDWbqMBxxPWLhSLYYefWIn45MFJsazdHZF2Lq9nYm5Mr9+9jTdXWEGN7SQS5fZsD5JsiVIT2+Md966qJT6zeOkC265bYC33rxIOlVZoCwcOLSV7s7gFQuu1o4wn+R0NpiKa3v0g1Ha2sNk0mU0TdDbH7/q994UMWWzVe69fyNv/uEiH7w/wp33bEA0iv1o1E8o7Cefq3DPfRt45aWzIAQtLUE81yMU8nHn3evo64tjmBqGIaiUHYYupXAciaYJjn8yzk17+rBtl3rd4713hnn8yZ3M52tMzZWJxS3aOyO0JK6+ePyysF2oeeBzSuTf+ic8CaW3/4nod/6f1Izl2oKKo6hUcb9ge3eE4yNZyjUPT8pr4lKv4f88rBWnqBvdGv4y4bgSiUZH1M9URWe+Cj/aIpaNe2ueymxOVSVjRfWw3RwXtAeVz97ViixQRPeevhh+v0F5yZityWu0giat3TEmii6tgeWj5qW4Inv8MjTHYq+PeQQNwXc3CqZKyijdleBJgeOpDuENbeKK/daEYH//yu99Mac8Su9rpDN1hQTavKQlIHh3SqVEfTANZzKS1oDywSw5y7tXAHd0CV4fU0XstuRVDxuvj0rmK03B1JX7VffgrSnJY+sEW5OCY3OSmzsWC8qlZuzXgpaA4DsbJC+PwA5d8lka2gOS/oigK6TsqJrdOYFgJZembKZCrC3esC9S7cHmQqatLYSmqWPS7GmWyjViDVN2w9C4fHdLxRp21WHdhjB//V/3MjeVI5MqE4sFaGsP8+nxSYaHsgQNgWVIbKGjxyP0hMAnlM3Y/b0s6eRfiS/qNrfNqCLj0a7LtrFs6im4rUvw+0veso795QKoO+9ej227FAo1YrEAApifLRKPBajZDoGAQblcZ3amyHO/P9UojnxYQYPrd3YTi1vMzRZp644so64svTbsmkO5rsR6HpKqowSDtarD0WNTHHhkK+WCTTpbJR4PEI8FSLZY3Hr7OsZmT2M7LgEdTFPHskz2HdjM5yeXkx+XjqBHipLTacEDm+L4DVU0n8/BzFAWWa1x6myasaE00bAfyzKZnS1y6WKKO+5aT6VSZ262QGtrhHrd5dhHE0w3rO1Mn069wW9thgE0F1w1V/LSsIdlSuyKspoyfTp9/XFicYsPPxjlltsHCAQMqlWHy2FZBj09MUaHM/T0RcnnbR54aBO1uks6VaKnN8ruG7rIZipks1V8PiUO+6snric1XyaTqdDaFqKzM0w47EMC01N5hocydHRGaO+IMDKcplp1ufHmPkJhk1DYx+uvnqNQsLEsky1dYUR7HA1JNPjFi8cvizZLkLOhtzBE7tIx/DpUR44TTQ9B+9LuqeTFEcmeduXha+qKVy2BSs0l5P/qypi1WuGrwze+OPX7/axfv/7r3o01rIKAqeHYLtlynVhAjbSbxaEnVXLP6YzqEM5WVPd0wzVmi9c9yYUc2A6ETIPHn9rNb351gnKljmzQAoJBkyee2oUZNBieV1zOWzuhaxUz/2vhVBbrHnd3q8+hOKKro+yomNO4/0pbqWaRcTEneXtS8oPNywvaQk3xcR1P5VcbQokMxoqqC+F4StR1OXa3Cp4fkRg6yzLpmxgtSN4YV2PiJzatXJiC4rl+OKMSuzZGVZd2FdvYa0ZAaWy4rkVje1KJJ1JVSWdIrNC5vswnCtVNm5CSd6YkNVcFN/g0sbBAsQImlWq9wf8VFBpiHytgYvp0Lq9Om0WQI8Ef0sgkW2jpbSNRq/CzX32OLl2COoDA7zP41qEtENJ5ddTjwV7BXd2CP0wos/X+yNUL1NW6zTd3XPsDU9cgVVmMvL1cAPX5yWn2H9zCyy+cIZVSfFrhST54f4Rb7xhg/8NbeOXFs5QbI9VCwcYKmtx4Ux+6LijkKggh6OiJXLHwWLg2XjxDEpdSXfGxYyGD/fs2ceT9ESbnKlTfvkQg6EPzm6TmipSKNUz/IAMbEhz87i7cbIFirkIiYRFPBHn7zUvLKANLr7WLeclkSU1MDF1D05T9VKrq0t5qcQ51HegISsXaQve1hBImDWxKMri5hfPnUvz6V58tK7iFECRb1IFcyiWvupI/jkvag2qRODOdp1ZzlXeoTym9a7ZLqWTz/R/dwC9+emxZgWoFTZ76wW5KJRvXk6RSJbp7YtgVl2yuQltbCL/fxJPQ1hEhnrTQEBQKNpou2LCpBV2DStkhky0zPJQhFDJp74iwc3cXdtVhfr6MaRoEg35i8QBW0ODIeyPkcjZP/WA3pinw6dDq9zib5ZrvqUsRokKpkYu29L+bSFUlvWaZ/Fv/hCsbEy4g/5bqnhZEkEt5RQkKmSqdrj8C0zkbDan41KuIK/8jsFYrfLX4xhena/jLRiRgULRdbMdjtCDZ3abiNEeLMFaUDMYEDw8InhmS7OsXy8a8lyNfU7GmPh1SVfhoVhVYtgshU3J9S4i//q97mZpQ3a/W1iC9vTGCYeVzqmynvqALy/Iu18x8hY7W5ZzKnS1i1e7rUpzNKuX+5rjg1VGJX5c8sk5wIScZKSjlva5JIiZ8Z8OV8YDrovDmhOSGVpUvP11WqVIVV3BnF3yeXvl9Wy3BY+vhwxlJwZa0hwQ9QZW6dDGniuV9/YKe0OqfQaLywm9oEzw/LIn54YFecdUO4bXg5+fVv9+b8khV4M4uNeq/lIOk30PEwpTRqNguUoJfVxZSekOc09EZoddS3+N4SfK7i5Jvb1D8ORUduounf36CYrm+0FX+9MQU3//RDbz+6rmrirgChsaedhgvucxqfv76x7sYGiuQz6pzYH3v4jmwr08VpT5djbc/mFFCtfUx9ZBebVS5Ure5SVG4FrgeNLMsVhJApVNlPj0+yQP7BhucTkmyJUgiEeTUZ1Ps3N3Nj/5W8TYLeZtka5B43CKTLnP4vWFu3tNHMORDItC1K03xY3E/Dz+6jampAqWiTXtbmK6eCMNDGcZmKgQbGpypTI2Yr4YuVLTo7HSBbNEBQyfaHiHRlaDV72Hpggf3D67YUS44ij7z8ICGXXIYHkuTmi+RbA2xpStGqCfCO7qOoa085WhrDzNyIU1nV5RzZ+avEDFKKUmnVbGYTlfoWJcAqQrTG9qU+PDGdo3TZxcL589PTnPjTT0cOLSV114+x74Dm/n7f7yFqcm8Ete1BunujgKS3/76c+p1l1tvH+DShRTrN7TS2hYkFPJTLFSZHM/T1h7CdSX1Wp1wxIffb+I6DqmszSsvnsW2HTxXUqu7hMM+HvnWdiJhH4YuCCctYjELoUmGhzL09MXZs7cf3RBUKg6+kEncD3s7runUWn78nBKlj5/Gd9OTAAv/vXRcP1OGLt8wqZFjVB1INDj31ZFj6DNDvFLbzobYcp5/0XaYyFQAybpWa22k/58Y3/jitFarMTs7S3t7Oz7f1ROI1vDVoyn2iQdNEglBxFQq9i1xwZ5GdvyvL0qua7l6YfrWhGS+Kmnxq2SnqAlb4zBXhetaBOkqnJ6vIcs57ticZG6+lc5WVdx8GQN+WOxyybY4bihOX4dYxqlcf41diI1RZREFSnjwxwnJry9KpFTUBkVnXX1bkyW4tUNxM396VjkbSAl3d4NKEVr9vZN+QdwnmatCoQ6HJyXbW+Bb6wW2y4qCmiamSpKXR1Xhv79fY3erIG2rh/a31i8XcH0ZSFSH/M4uwZmM6v6+PyPZnhR8MCMJGoLrWg2++61tvPLiaZy6S9lRgrCwpXHw4JZlgo7ekIo2PZGCGyIuEzNFkJK//fs9TE3lmZ8rkWgN0dsbIxoxrlnE1btQtOsk+uLk43GG65JsHnb7JKaujt+31kO6qnh3FUctkl5YsH1SkaTrosrOa7VF0aW8sh3b37/6a5ai6i52qK4Q8DUEULMzRS6cTxGN+tlzaz8XL6bYvsNgx/VdTEzk0TRo74jQ0RHm6NFxzp+Zo1ZzOfjIVs6cnuHBfVtIzRQZH80tiIYCls58gz4wXXDoSvqIBH1MjOaIxTdh+g1cCRFdUHElfl0JtJy6Rz5XRWgaH300TipXoz2ic9e+rRzRLKZKHuujGlYkzs4NiUYClLrWJotwc7vG3GSJf/vFCSpV1e31pPIv/tHf3MAjj27jmWdPL2vp+3w6D+7fzB9eu0As7sd1JdHoyn6asiFIisUCZIsuF8sat3ZpRE1JyFDX3NJufiZd4ZOPJ7jp5l72HdxKsWjjehCNqQ5mtVInl6/S1h7k0GPbmJku4ElJZ1eM6ak8AcvAb5qEwgF6+w3KZYeaXccwNEyfsvKqVF1efO70QjfWNDQCuoFtu/zsXz7hqR/uJhLxU6nUOPrhGLtv6lZWX5U6QhPUag6BgK8R8fsnupimh8i+879oX78HhLb4341xvURyfaRM8e1/Il9TXGwhBG7Dwsp7+5/47hP/F3VjeQrUxdkS0vMQQGfMv8Ib/8dhrVb4avGNL06llNRqtVWzmdfw9SJTrgMCy6fTF1a3yYf6Fm+XtqtGdqtlNwPMVTzydfjuxiYJT9kflRyxIC7aEAXHEeSyShHaHoDTaY+b2/60Qur4vGQoJ7mzW1w17vJiTnIup1Q4cb8g0JhSCQEhQ1lQGZrikwYMQcKv8revpWNQqkPUp+gDt3XCp/OyMc69ts+0Lqp4ovf0LO8YrzZJmy5LXh2TuB7c2inYllj8XdIv8OkS21Nj/i+zH0vhSoHf1Li+VXIy7eEC0xIOdkomJvNMnazg7w3z+JM7Sc8VyTQKyVBLhPczGvEpyZ6ORWrITW2C6ekKv/rZacqlGo6jctYH1iW4/b71HC/ofDgpOLROkvgTRFz9YZguefh8qmv5y/PKV/a6FpV5f3nC1tZGqlW6KhkuKHFfsQ6tluS2DkGLtcT5wZYcmZI8PvjFHf0mHh5YtJVaScDnuh5uozWbz9sg4cLZeT49PsU//OOtzEwVsIImHZ1RLpybRyDYc0s/VtDk9OfT3HnPRsbHsuiGWGaK/9hf7VA+vzUHT4JdqmM31PbPP/s5h/5qJ8mQjlf3kAgCmsR1PLKZCj6/Tjjsp1CsYWjg1T1eefEMf/OjXdRald9mzCdBLkZBAMxUoNdw+OclhSnqZVSrdX7+r8f4r/+3W3jwOzsxCsWFRUdbW5g/vH6BiakC26/vYHqiQCTqx+83loU2NKHrGvGYRWoyw/qQj9kLNvmAQX80jOtpV/DQM+kKr796nlDYx8BAjOuu72ZyMofjSCzLBA9qtke97hCLKwcDIQRd3RFKxRqnTs8QDPro74/T2mpRLhtUyw5O3aNQdUjNlaiU62h6w3IK8Fzlp2xZJrlslXrdxR8w2LKtnWLeJhqzCAZN0ukyuq5RLFVpDYeXdeklamF8eFpNcy4X1jXRFDgBFN78/xDYdJs6n95aFDtdyMJWb5jpc8fQtWZqnaToqMaBNnEckR5exj2dL9bIl+sIJFs6Q1+5ldRarfDV4htfnK7hLxepYo2i7SLRaA2vvFKNrtLBK9Qkl/KQqaoozj1LRlO2q/whVbKTggbgahgyzLnPU+TzNonWMPO+CDVDp/vKYKqrQGXdjxaUN+fVsDEmGtZIjTCBxn3PlcqHtVZTwqI/ZFWM311d116ItFnw+yHJIwPw/rTk8avwQ5v2+Uv7JO3Bxj64cuUM+CWoOEq5f0/P6pzfDgueH5IMxptF1rVDAyazLgPVIqc/qdCStLirJczmdoOL42We+7czlG2Xch2OfQy6ofOd72wnF0wwUvWozEm2JySzFTg+B3saPE2v6vL6c58zM11UnaLGTuVOVJmZLfDkD3dzIS84MSe5t1d8aREXKA7v0xckhgY7W6DowAtDkqCpCs+wqb73mqdCG27rFCQD6p8bG2mJowXJxbwqDFypJgdjRUkicCUX+WpYer0sFSlVbAfPBeSiyXszgSmft9E0weH3hrh5Tx8vPHeaz0/OcNc96wmF/WTSJeL+AA/u34L0XDRdULYXy3bTp3PxfAq7kbxmXHZNlKsumWyFRx7dxsvPn8Eu1ckXbfw+HcPUeHD/Zg6/O4wlJNXGF6R7LnMzBfo3JIj6ln8hOVvy3rSkJ6wzNZ5eLExhYTEhhOKUjo1kGY608EBj0SERjF5MMztToO7BbKpCS1uQd94cWuDbLksVCxg80uhwFgo2b//xEnfds4E3Xr2Aq2scPLSV3q7gijz0es1l3foWfvebzxo+ry1cv7NL+e/OFNE0QXdvlHrNoVp1sW2IxixMn47jSlxXMjKcYXamRGt7iKRh4TM1SqUaPr+O66pSvV5zqTcWXZomKBZr3LSnh0y6QiBg4PObzE3nKZUdWlqC1Op1qhXnCrGk68EzQ8pa6s1xia6pqcWWxGWi0/QQ1ZHjANgXjxDcchfCF6Q6cpxIeojjYjt+t0zpHVXABnRwPbVw9WmLPtVLi1mAbKmGQDmxdETXOpf/2bFWnK7hLxbzxRoq9V2N9a8FdU9yeApKjqTDgqAp8OvLBUx+ffGhD6rwyWdspqcLvPT8WapVlTOtaYK29jCPfns7Wsj6EmpVQV8YLB2Ozsplnd6r/U3isilV+5KmluK7fjns79d4b8rjpVHVef31BclN7VB3YUtc4jfUsX1pRJKpqg7LjW2qeNiRVOKqB3rhZ+ckT2yC8FVoE3MVpb6+mnBie1LRMrYlv9xn0YDhibLyqrTdRjoPVNBpe/I63n7lLPNFF7+uOsWlOjh1l5efP8UPf7CLQIdOyYG5isBXh+mKSuYydI3JycJiYQoLGirPk8xMFZmdLLK3I86JlEfNk1+qEGzCpwu6QpJsVVEtukNwW5cSnGhCo92SC931nK3EZvd0q056E8qjVvn1/vwcnMuqwISTadVlTQT+tAHsUpFSsaCKUIQS5ew7sJn33xsB1PEYupQhEvHz1A93Mz1VYG62RKLFYufubnw+jXrN5ZlnTvOdx6/nlVfPL7yHZZlk0mXqdRef30CgCmy90f2veZCaLbL5th7+6snrOfX5LOWiTbIliJTwwZFR5mZL6Jog0nBOMIUgl6mgNTyDl+KdKbWQ648KDp8uLfudBstoOqlUmVhb68KiQ9eaVAdB0ICLZ+f49rd3kM9VOXJ4hH0HN1Mu1ck1oj/DER8trUFef/UcfX0JhCbIZlTRV6k6PPvcGf7ux7uudFtIBknGLYplm5v39vPic6e5cD7FiWNT9A3Eue/+jcQSAUxDp1SqU8zbtLSFCEcMYnE/9ZpDoVBHImhrDxGLBQiF/NRqdaLRAK6rzqlcrorrNdrFCDRdEI36mZkqoOsaw0MZgiEfXZ0RSuU0o6NZLl6c5+H966/oEBqachhpouJIPk3B7y6q87XVEmj1Iqk//BMVR6LjoRfmyB3+KYHtDzB39Fnqr/0ToYf+L3qdIXIjx/DpyrNaorjXgSUVyeXK/aDfQKIWiFdKHdfwnw1rEQtr+ItF1DKhMaibyX9x/mLOljw3JBmIwMMDGkFTYGjwrQ1ixQ5rzVNZ5OMZh6npIi89f5ZyqYbnqXhOx/GYmS7wwnOnlsWOXitaLRU1enR2edznnwaJt0I06NVQciQTJZVRnaup7vFUSaVOPTMEz1yS/OyspDUgeGJQ8K0Nisc6WpT8/pJH1lY2Tbd2CN6cuPp7n0xJijUl7FkNYVN86cIUoFR2efbZM/iku4RSIGgNG1y6MI9Tc0gG1AOuWIeYX6VsZUvK11QXAl/dw5jPEhmfYnMtx1TWxdA1ZmcUp4/Lm8qNAnVutsjudp27uwW/vSgZzv9p3+OuVpWularCvC2Qtkt6LMfk55OMD+Vwqh66phHzC/b1KfuvnH3le5m6tqDqHyqoz/zpvHIteGnE4/CUive9VjQFfE88tZN779/I9us72XdwCz/6m5s49vE4qXlV3Amh7I02bGzl7bcuoRsa6zcliYT9zM0WKORrvPXmJR58aDM+UyM3v2gwX6nUicct3EaMbnPU3IRPQCzi48znc0yM5Th7Zg670WF84fkzpOZKC1xS13EXvqfLk7GaqLlqMbrUv3g1tLQEKdcWN9KkOrhSUnQk6XwNp17nBz++gXyuynO/P8XRD0YZH8vy2YlJWlqCVKu2mrS0WJSLNVUQuqogrNdcxicLSCEW3BZ23dzNuo0JxsdyGLrO+XOz7Du4mVtvH+De+zeyeUsb83Ml3nzjIuVSDU0IOjojeK5kfr6CbTsqgUuDUNAkkQxSr3uUSjaFgvLZVYEKakEgPbmQaOXz6UgpCVgmkxN5fH6DZNIimy1RKrmcPzvL7Xesw33v/4XPKV3lyKkgkFs6BA/0Cl4alZyY9yjNDFEYPoYuwC9reLUylYtHCLT1E7SCmJPH2CEvUTr8r+ia2kbQFIRM0fD7XX5/yL/1Twv74TS8/SRQrH75+/Ea/s/CN75zahgG7e3ty+Lp1vCXgc6ojwuzJVxPMpKqELVMgisQHgt1yetjEstYrgjvj8B4ET5LQd72aLEg7lNeolkbzuUkN7RpyHyZUsGmUm6YeC954HmepJCvMTNVoG/jlbGjX4Q7ugSfpeD1MUm7Bbvarq3D5XhKvY1QVld/nJBUXLi1QU/oD0NdqpHwytuTFGqqi7gxKriQlRydgZ9s0/iXsx47WwQtARUFe2ObekiEDEj61ej5fFaZ+HtSckuHEkE9O+QRMtWYuiUgqHuSc1nwaUrEcEcnxFfWjVyBiiN5b1JyTy+Y2uprZNuFcyMFfLgIBJYukY1TwLJM8pkKtZqL7tOJmKo4LdTU57EMychMBV/E4re/+RzNdRuOBspv9Mnv7yIS8V/pOLXk/8MRP0JIekOCB3vh9XHJuqjA8eSXEna1BjQe6pN8loKNVHj72TPUbA/RKGA0XfDIt7bT2hYkHNC5r1eJ+O7qulJ81h1SqWPnc7CzVTBeVMEMEtWZ/bLuOkrAZ+APB5g6Ps3AQJx8rsJD+7eQTpcpFGxaWlU+u1N3uO32dczPFZmZqhKNB4hEAlQqde65dyOaIfjow/Fl2y8Va4QjfqygQc1VNJUmS8RxPCXckcoVIRazmBzLUsxX6euLE2hwNZsUa9lok14epbni57rMHgwAsfhVW5ZJd2+M8fziFy6lJNkeoS50DKH8VF95+TwP7d/EP/y3W5mYyC2o6nt6YtRrdV587hyWZajzMW8TiwW4cGEeAYR8BlNzFTYNNlLhGh1aTRP4AjrVap29t/Tz0vNnKJVr9PbF2bm7G9dxaWkNMTqaZfOWFgzDINCIr81mKti2x9xskVrNobsnSkdHBM+TaHqNWCzG/kNbefrnJ6jVnAVvTr9lsP/AFj4+Os6eW/rZdUMX9brHzHSR2dkCluXjwf2biRROkvr0JUK7H7nMb3Slc0fFGpsarPOX4Y//X4KGQJMubn5uoftVPPwzYjsfovDJsxTf/ie67/0H0qmH8emqQ305Fr4RAT7No+JJ5gpqrK9rYF6jO8W/J9Zqha8W3/ijrOs64XD4i1+4hq8cQgjilkmqVKdWd/l4KEPUMrmuN7rASwJl6v6dDer/XU9yKi35PC0JGLApJugJQd5WfoYzZah60BeCJ9oEPl1wflrd8Fd7zrmOx/xcmXWDSZwvSTgUDaX99S1wZFr5fu5pXz3VpMmXM8SiMbsEesOKY3h4SrIpDh9Mg6ZBX1g01PfLcTINR2cku1oFpbp6eDzYoBfc0CqYLSurp6oDV3pYCwbjSgneE4K3J5XP4F3dgnNZeHZIogtJ2FT+q7MVuLFNcDor2dOuqBVfZJVlGdARhJ+ehR9uvpLTmrbVGHwwoaGVK/gXtrdYilcqdXp7Y0hP4koV2Rox1Ejfrkh0XdDbavHOGxeQrkvRUdZbqqvj8eFbF9h7S/+i0OWyXQ5YBu0dYYq24tmVGlTDfz3r4Xiwvx+6r2KndTl6wwLDcXnh385geB6e45JJVxa66r/6+XEOPryVREuQzu4QN7SqTvSDl9FC8jWVkCaBvrAqxodysCEm6P1S3OhFjBc9AvEgxXyV1145z623D/DGqxeIJQKEwz5aW4IEQz4+Oz5HW3uEcrlGreYRA1pag5RKNYQmqFbqnDkzd8X2z52d49Fv7+AXvzuDqbkqXhiJbTvsP7CZ998dplZzGViXoF73SKfKnDg2yYMHNvPqi2dxHA8EGIayBDvw8NZV425rS34YDBk88f1dy9T6AAFL+RfPYtATWn5Nh0M6T35n2wJHtJC3eeY3p9l7Wz/tHSESCYtyqc6li2k+PDIKSA4e2sZLL5xRPN2gSSFvEwr60Exob7Ua85/F99F1QTDo46MPxrjz7nV854nrSM2rKNhKuUZXd4z1GxNomkZqvszM9DwdnRHa2sIgJHFd0NEZolio4blqpqI1tlmveeSzFQ4+spVS0SaXs4nHA4QiPk6dnKFUUo4Atu1Sqzk4rkt3T5RoNIBv/gSpX/3f1Xn21sppTU2kqpIXhiUBHa5LQFtlmNkG1xSnhmcvds+rF48Qvf1HCF+Q8sinhBGEt93DkWk1ot+eVIb802XJVFlRi2Km4M5uKCKYSJUp2w4Cyc7eCJapVmC5Sp2ZfI2BFgv/5WTmf2es1QpfLb7xxanjOJRKJUKh0NqK6C8QW7tCjKarTGSqeBLylTpDcyU2dVx5kyg7Kt6zP6y4UUsLwM5VTPM9CaGwj1hs9ZafpgnCEd+fxYERqLjPkyn4/SVJ3K8Ur9katPiV9+RHM5CvK45qcIXOwG0I0lXJM0OqeNVRav87u69MwUr4lT3LDW2qawuLFkLXtwhqCfj9RclAFN4alwvJUktxV7fgxRHJ3d2C4YIqlNotQUcQ9rQrUdPS9+0Lw/PDyj7Hp0laLGWMf1MbKwiqBNe3CvJ1yc/OSR5dp2gQTaQqMJSXbIxfqShvotmRCwUNEGBKSSpdRWuM6X0Bk2DQJDVXoibVtnM1Rb8LGpLx8Tz33Gfy8KNbefG5M4sFqoSAX2WJB8M+/u2CS9wHh9ZpTBRVbv26KLwyKumPSDSU0GhX69VtsoQQOJkCZdslpLOsMAWoVh0KBZsjR0b53g920RUyODonGS/JJfZUUPcEMT+YmmQkD7d0KC/ZjpByAPhT0G6BqRsc+tZ2fvGvxzhyeITb7hygUq6Tz1dBCHw+wXU7u5mayuO5ko7OMK2tQXRdELAM7IqDrmsq+/wyP9hbbx8gkLR47Imd5OeLuMUK4ZAPT0ref3eEmZmiyoP3GwuLhdnZIpUPx3jowGYq5Tqlco3165N0dq9s4dWEoS0GVLhAZ3eI//KPe5kYy5FKlUm2BOnujeEPG7ww7PHIuuXH7HKv4ky6Qjjix3MlRz8YY/PWdor5KnNzJe66Zz1t7WHee2eIfK7KvoNb+OCI4unqukopi4VMRi+mF6zHpBCYQlCp1Ll+VxfHj09yw409xGIBgkEfjuNSKlbJZVXMqaYLrt/VheO4FAuKMhAM+dB19R627ZHLVKhU6wT8JrG4RTjq5/WXzxMIGgT8JtNTeSqVurJrcjzCIR+2XScaC5BIWNTtCvP/+78wPnlpgeu8clqTCvQYyqv7wXc2Cj6ckQQpLyj0NeniFq9coOQP/5TwdQ9S+OTZhcJ3b0fDaD+lmgl9YcXPPpdV968mFMVLoeaoic7wfIXRdBWAcs1ld1/0Gs70Px1rtcJXi2/8EXZdl1QqRSAQWDvh/gJh6hob24Ksa7E4OpyjWveYzFZpj/qJWiaelAihxtd/nJDc2inoWMXiZCmEULGVnoTWNtWBWMkqRgiwggYtrSHkn5luJBreote3KBX8mYwq6EaLUK7D5gT0hK5eAicDgr/dpsa3b45LwoYqCMOGpOpCuGFiPllWRu+FmhIreZJltkU+TbA+CgFDcip95TGRUvmGbk0oS567uxU9oVBTPpzPD0t2typ+6oF+JTqzDMF3N6n67kwGkKqQ069ie3VHl3ogPTcsua9HCZ0uNFJh7u4WXxgJe+7sHI9+awcvPn+a+UwFISVSCAIBg0OPbmV4OE0uVyUat6h4ap+kUKPlmgejk0XaO8IcfGQrhYJNLlclHgsQivhp7wjht+CHmzWePu/xzpRkaxwmJxWv0a/DQERQd1VX9ZfnJfv6r8yub6LpKxr1CaWGXuIO0EQuV0XXBBNjOTZtb+O+HrlgL9bEng71Z1vicDEv2d2m0RNSQQRX8669GkxNdaQNU/Djn9zE0MU0U1MFOrvCdHS24jjOgh1RJOInHg9gGDpSQqVaRyAQuiAS9q3oBzvv6bw16gEa0VCM+7clOXtimrffvEQ+byM0gXQ9Pnh/dEEV77oeqfkyL71whmRLiG9/dwdtHSEcz1u1MC07auEmJQtUABfwhww2bW9ji6ZU557ncWJeLbZWosU0vYr7BxJEIn5c18M0lXvA/GyJREuQWza1IjTJ+XMpNm9pY8PGFt5/b4TUfBlNUy4Ft925jpf/MMR8qkJ7xODgw1twXBe7qrxRX37hDA8/uk0p7H0GEkmt7uHzaUSigYZqH4pFG13TFBfe9UilSoQjfgqFKkMXM3R2RugbiGFZBqWiQzjsx/UkpWKNakW5JEgJtZqD36/T2RXBCvpAgFstUPjt/wOZGsLU1DUeNJQ4bGn3tOJIPpgGpyFme2y98pbeEocN5dW7pk0s7Z42C1+t/To2xdSEaxFKUPrulOSP47A5IekJmUQsk0KlzqnJooocliAbLYNs2aFSc/9DE6PWaoWvFmtHeA3/R0DXBFs7QxwfK6AhOTGao78lyAfZAKamctdvaP3iwlQD6rZHJl0mNV8mFPYxuLmNllaLg4e28NILi1YxQkBbe4i7790ISOqu5PMsaEKyLfHncJ7E/5+9/4qR5Dy3tNHnC5uR3pR3Xe27abrpnShRJEWqaSVRFLek7WfmnD0z/8XcDjA4GAww5mIwwAD/OTjYRz8ws//tJW0ZekqUoUTRe7K9L++y0rtw37n4MrOquqodRVHkZi2AALsyMzIyMvKLFe+73rUYiMJAVP1re+pyXy0otodeHt0pqPtKl1psqQU9kLA1oSqRp8uyu5AXmqqa2UHJlVyZE9R8mMz7iHKVwnKja5xuOzqH8qqFrEz71cDCbYOS/3VYpVT1OIK/OyZJWOqiBkr364VwtCAZicORIvREJP0bVq/VFPrdI0o+4IWq3b87vXJcLhQJe8NNo5w+lefA/Xs4e6ZAsU0uk6kIJ48vke2Jg5SEfoDQdHwJMVNJPEIJjQDOag7jYyaRpSqxuEU0apHqiXGiVOPscZ17t2o8ukN9zjNl2J8T7OsRDERVpf5P96gq/a60SuS6KgcjsfXV4s6wjUBJRYCVTm/7qR29Yj5fZ7fGhsESHcnEcku1RL1QJRL96JRkR0rifMj2ZrkV4po2v/rpMW68ZpB40qZcauA4JplMjGqlRTRmYxha25oITFNFgs7N1/jlr88imx4HHtjDlnP8YKMtZTvU9FU7uNIKiSdsfF9Nleu6IAgkC/PV7lS8oWvMzVVIpSPE4hYTszWOSYeZqpJVpG0Vrzu+yiGi6avI0Lqv9Nhrjn8Ydr323VAyW1c3QOeDEMqQvrDcJBY3KSyrIaXtO3JIAadP5Rkfz3LwvTksS2f/tUPs2z9IreYyNJLENHXefXsGr6JuSGo1j7//23c4cN9uXvz1ab71h9dww42j/PB777P/2kH27R+iUnNpNX2CQEl/xsaSNBoBj//wEKdPLrN1W4aHvnolmaxNsxFACONbM9i2iRBQLrt4bkA6HeFrX7+Kp588TKulNLtCQDxucf9De0lkIgRhiBuEWPmTXeunmKn01M0Ami5ETrxNLH+aSvZKnjgjuaFXHe/VrhXbInXKz1y4ato9xzaonp5PNvC5QZitKb9ngH2jSd4+W6LeUjZXpqHRm7CZKaggiYMzVa4dWyv52sSnF78VOf2v//W/8ld/9VccPXoUgJdffpn/8T/+B6dOnWJwcJC/+Iu/4OGHH/5IdnQTm0hHTfYOxjgyWwMk1ZbPYzsu/UKsA5WSx5nTy+TzNZKJCHbEpFCo43oBQyNJ/vBPrmVxsUa10iKTVS1LTRPUai4SsO0Y2ciFdaenympIINu2hrJ1NaSSslS1LnIRz9CNsLqq+cJUwEITHtmuqj62Jnl+VlWM4qayHVLDrJJfTkt2phVZ/c0cPLxVbU8iWWpCwxfsFHX+/rtHsAi62k7L0vnyA7uxDYere9btDbmISmjanYbbBgVTFaVxLXtqkCptwRVZOFNWllqxCziBTVQVUfBCpYedqct2a1Hty7lt1nPN0sNQUq26HD26SCxqcezYEo2aSzbrsGVbDts2IJTYpsDQJHUf0hY4tk6iJ86SLznua9iZNGQFWzIwXW5xOt/g+q1RHj8tGY4Kvtm+EejIjofikIvAXx1RVd/xpKAnopwL3liAhqcI2fV9Ko60UwU2LR3D0FaqplL9F3GUr2il3CKXixJcpFLvhl0VAqYm+Nyg5CcT8MD45Q1rddAKQI/ofOmOLTz15BEsWyfmWBQKDfbuNdB0mF+oMzAQY3g4iev6fPDeHMeOLjFfaOGGysrr2aeO8Og392E6K1WslC14ZT7ktkHBfAMMIekbiBONGlSqLloo0duDXwvzVX763DEefHgvy8t1Fhaq1Koud9+7g/4euLFXnd+WpqrRq5GNCG6+hKG8V+ZgV4oN5TMdaAIWF6pYtka97pHJOtxx5zaefPxw9wZ24kyRL3xxK5qmMT9XYWg4xZ6cwzNPH2HiTJFU2sEwNPxQUik1QUoq5Sa2pXPy5BJDQynuvGcnvhfw1pszjI6lGB/PUqu3MC0NGQoKM1W2jGe47oZhFaccNahWW7huSN9AlCCAetVncaFGIhFBNwSuF5DrdfiDb1/D3FyFxfkaiZTN4GCCZMrCb7P01Yb5HRiacjmJGpJQwtzz3yF/93/hwFiM7Abxw5YWEr/hEeLXP4JGsGHVdDUiW6/HHr+hO+zknud5ArHGX1oTgmu3pCjUPAxd0JI6fVENXRNM5OtUWwFn8g3GspGP3aB/Ex89PjQ5PXz4MD/+8Y+7/15YWODf/tt/y3/4D/+Bhx56iDfffJN/82/+DVu3buXqq6/+SHZ2E5tIOgamLtigw3tB6MDCXJ0f/eADGjVP6cF0DTS4/4E99A8k+O7fv8vNt44RiZgEQUizoVJW3n17mke+sY/ZmTK79sYJwwtf+Dum6idKiiTYuiIQi3WJG6rK6x1DymT9ovstBK2Gz9xclUK+TibrcGUuQcrWcAxBM1BEKGUJ+h1FOGdqqrI7W1e+gVlbMBxXCU0dHFxWRCIpA77/1FFlOdPWigoErhvw9JNHuOXBfQxuME774LiKfP3NrGQgBrvSgq/v0PjhyZCROOzNCBabytbJC1darB1IlBvBO4twtLiyX28vqf9fnS4FK23W0XVm6araOTKSolJuUSm36HTLPS/g4HuzHLh/N7/51Sl8P8A2NSxNEBoaBx7YSzpn8KujId/aJbA0OLQc8vo87M+q9+2LwGM7BD+fkryXF1y3KjFMQ7U25xuSp89I9FnJXcOC2wY1jhTUYIcUgmcnJBE95MZ+QcpRwzxPP3GYUqnZtbGK2Ab33reb116ZwHFMhkdThOGF2Kmk3xGc9RWptzQYjGmMJyWHCspx4XLR0fxqkSh/8K3957Tm44iIwZAGpi44eKzISz892m2Je6HSetZ90IXP/FyF0W0ZorJODYeqJ9v+tIKkJTleklyT03ngK1fyd3/zDm7TQ9eVSbxl6Xz5vt289OJZgiBECNGdzlehFisT3kEomViuE0rIxS0cS78gMW8FSs84W7tw1RTUb9j3Q3p749TrHq/85ix33LWdP/2XN7AwX6FScenpidHTG8PzfUbHUvzi5yfRhODMqQJhKCkWGmRzUYK2pVMQhOTzDWIxm7Oni1x51QCRiEm10qS3N0al2uLo0UUSCYvevjhBGLB9Z6Y9We8zO11hYb5GX3+MdMai0QjwWh6mZRDBRNNEe9AppFlvT+oLGBlLkko5RKI6a4RLqwzzz4UQSjOrz77NiHeGenjlhs+ragnYescFj+Wa5wNy2wACNex0OdCEIBe3OF6UHCtKHhiH8R7VgprM15lcbrJYdtk/mvidtvg38bvHhyKnYRjyH//jf+TP/uzP+J//838C8MQTTzA+Ps6jjz4KwG233cZdd93F9773vU80OdU0jWg0inYBO5tNfHJwZqmBG4Bp6GzrvbTRZA0ol1z+4W/e7lZAQS10yVSEnz53jG//8bXce2AXf/fXb2NZOtGYRb3m4nkB3/qjayksV0kk7XYkwIXhh3C2IombynS+11EVNk1oFFuSsiv5YBk0EapBqX7WtaKmaxCVIa1ijaceP0zgh2iGTiMEoet842t7mK9H+eW05MZ+pR89WxZMViSaBjvTgh+dCumxoWMJ2NsmH/N1yevzkm/v0pifKFJqp8F4ofLhFEKStSHwQur5KqJ/vYWWJgQ9Dlzdo4zhfz4l+fp2wYNbBceLKplKE+qza4bgu8cl1/bAvh54YRrOVNT2bh0Q/OleQaUFPzwtSZjKyP98nen1Zukrg1EdzXCnwux7IWfOFKk3PL729as5e6ZAudxkdEsaM5eCuI4Q8PDWlffbkYanz0iuyggawuLVBbh7i+DOEfjBSUnSUvo4TdPQ2/rFfifkj3ar9/zHE5I/2y0Yt0PEYoX8coOxtEP/YJx3l3Qafsg9ow5/8O39TE6WOHOqQDJp40RNXn9lgmrV5bFv7icaM7jw/ZcKktAEnC7DVW0y3R9VFlMboeFLllsqqetCBO7cG4FOa14JdpUzQH6htkqrqeQFEV1VXxs+FAsNdoQRaq/9A9p13+AX01Fubady3dgveHEGTlckWwai/Pm/uoGTx5eoVVr0DybwPMnrr07gugGGoanp/AfWTueXGx6zpRalukfLCxBIJvN1NCEY740ynFkZousMRy03JT+fluzLCR7ZvlKZPx9Ol0IimQSnTy8xOpbm2uuH+dE/fcDNt44RjVqYpg5IDFNDN3TOnikwN1Nh7xX9BIFyi/D9EN8PCfyQoF0Kz2QizM+WQYOZ6TLjo0lMM4bnBWQMjVwuhmVp1Ooegade26j5JJIRhkdT+F7IzHSZudkWvX0Jmq2AYrFFb38MO2JQLNQJQujri5PPVzh9Ypl9+4eIJ0xaq8rxG1VNN4ZAe/k7NO/+L+B8NNPqP59SUpSsffHnboRWyJqb+860/umlOk0/4MhcjWvHPtoBqU2u8PHiQ5HTf/iHf8C2bR566KEuOT148CBXXHHFmuddccUVPPPMM5e8XSklrdZas3VN0zBNs5trey5sW53dG2XeGoaBrusEQYDvrx106WzXMAwymQxhGK55b8uyEELged66Coau6xiGseF21V2+ilY797MAmKaJpmn4vk8QrL38dLYbhiGe5617beez/q62e6FjuNF2L/bdXOgYXui7udAxDDwPKSWmoWFoEt9f+3k0TUPTlNF0EPhomsBrCk4cXaJe91ba46EkDCXlUpNsLsrRI0vs29fHX/wftzI1Vex6GY6MpCgUatSqPk5MICXr3lN9HtW39n2flBFyQ05dpPNNOFrQaQQCZABhgGRloKbH0fj5tM6dw0Dotz+DwKm1WF5u8lRnihxFRNLpCLoGP3n6MLc/tJ+Ht+rYmqTc8HllRuXaDzkQBAbX9Gj8Yjrkxh4f319ZyA8uSu4aNomagsn5Ok1fteFBDfaYmmhPtUvKxToyTHQNsEF0hwF832MsCqGUvDQtabQEEctkaxLemA/wgpClEPozcNeg4EhR8HfLOpYmuXMwoM9RuszQh5gOvRGdhQbsTnpYumD1aaHrBkIIwjBQ2kEhSGcibVoEBz+Y48D9u3m2HS8p1K6iGxrX3zjKr144Rcv1cRyT5bdn+NL9CX5y1ucr200ytkYYhvihjwbsTkpenJHcvjXFS/MaINFCn/tHJG8taYzoMDNVIr9UI9cTY3gkhR0T+GHIzgScmmrw5BOqGp201HH0hM4jX9lDI2ExXQ4YTprs3J1jbCytiMZchdu/sJWhkSSxuEkA3c96ofPbkJJak+732+cYvLso+e5Rj/GkpO4phwIhQNMMMhGNnBVCGFJ3AxpugK4JMjELXTe62z3f+X1o0aXQgtG+CEfbx74jc9AExAxJyYVkOkKYP03xV/+LnvEb+eLQlTia3/1Od8YlU1XYkjDxoxYju/uR1Qa1uke2x+Hzd26jWm6Szjj0DcQxbHDbv7v5cotjc3XMtmdt4Hs4hkbDC/ERHJv1AclwJkoQBLw+F1DzFDG/Y0TQ4+hrjuFarJzfQ46HYZgERYfJswV27e7jq49exex0meV8nb7+ONlclOXlGs2Gz9kzRcJQEo2Z2LaB56n10vcC9PbdTyRiEI/bFIpN6jUX2zapuwGGIRBCx3UltVoL3zOp1TxaTY9U2sE0BdPTJVJph0q5iQTGt2WxTJ3ePgffg3y+RsVv0T+QwHUDjhyeJxIxueGmESKOTsN117hDxFa14zfyi+34o0qpOj5jsbC79nXOQ6XjPfc2au0acS503SCiwyszPveMyO77bHR+n+88HLB9juVDmlm1nh9ahvGUxRVDOu9OFFgqN6jUTKz2cf8oeISmaeu4wm9zDfys8ohLxWWT06WlJf7P//P/5K//+q/X/L1YLNLf37/mb+l0mkKhcMnb9n2f6enpNX+Lx+P09fVt+BjAtm3bAFhcXFx3sHt7e0kkElSrVfL5/JrHHMdhcHCQMAyZnJxsJ2isuhPbsgVd18nn89TrazU0uVyOVCpFo9FgYWFhzWOWZTEyMgLAzMzMui9pZGQEy7IoFApUKpU1j6XTabLZLK1Wi9nZ2TWP6brOli1bAJibm1t34gwODuI4DuVymWKxuOaxRCJBb2/vhsdQCMHWrUqIuLCwsO4H1tfXRzwep1arrTuG0WiUgYEBwjDc8LsZHx9HCMHS0hKNRmPNY51jWK/XWVxcK6C3bZvh4WGAddvVfYlGklrTZ3KhSExfe7LHYjHi8QSe6+K2Gngtm/xijaWl2kpEJWoAo0NQXTfA8wJ+8+IEQ8NJtmxJs317jsXFKnOzFTRN8OorE9x7YBeCcN1xANE994vFYndRFUAPsCOXxrYj1Gp1qtUqoCZn6z5MVmxcI8XPJnz22Xk0DWJ2kl88f5ztO3rxXFUJRLbjCIvqgl1tuviFMtG+HPV6jXfPloi6MBSF44tQjFvk4lmSFrQqy3R+GVLCzKLkCyN9hFIjmrCxRdB1IohqasGt+xqmkGRTJvn8Mn77fNN1g54eJULN55fpTPRcYUuePgR3786RjJqIVo24bGD5cHBKEZh0PMpgPEG14fHqyQKeVJZUaRuE0LhzpJd/PC45Nltk0Fm7kGfSGSzbpl6vU6upxJhMNommSXxPUliu8/77c3z5/l3Uqi6lUovhkSReIHn7vTla5SZ+4FMqqt/ywkyJAS1kohhnPBul2WxSqZQByAJHCiFGxCIIM0gpyOfzxJwIe4XBd/6/79Jqed31ImIbPPat/TgJnyujFv/03UPoocRHKPeAMMCQAT/40UEefexKtKAB9OBLyVJpkUTWJNefxvU8SrU8RqQHwzCoVqvrfjfd89vzWMovc3ZRsjsjyOfV99bb28doApJeAbccMlOF2/rBMQWZTBbL0qlW6xycLjBdaCGlapsn41Gu295P1BTnPb+Xm5LJpRL7MwHxuDr2rhvgSZ2YIRAyJAhDErZOIm2x/Nz/myAMqP7qO2S/+l+Yyle7W9SAEQFS9vPWQsiYUWEgDrojeHGqTMKCz+/rIWLb1OoNFhcLFOo+U0W363fZk80wmo1Qzy8TMTUCXXJk0aUZaBwXAiE0YqLFVqPBOwXJzijIqqAuV9aIQnHt9UnXDapWjqNFiVdaZkdfjGgQEk84nD1TJAgCRkbTCCF4440pnnnyCLffsZWXfnOGq68e5GjN7ToO/PTZYzSbHkJTEiInanLPl3fx1htTGIZGGErefG2Cz31hK65QVXgN6OuPo2lam6AFRCI68biFKtiFJJMRpAzxvVDJZYROveGiacrntFJuUSw12both2kKSqUWfmDiyxqtVet7xbLJbL0DkOuuYwA9PT1omk6pVOKD+QbVk3Vafg1dQF8mTiIeZ6nSolwqKms7rR0dLHRKZg+5CIyRxxQSW1cJcUKAnciy3DL4XKbO8vLa8zsajZFIJPB9j+Xl5bVnodDo6+tT/2iUGMHjlZNqELTHgUg6g2FbtFotKsUCT+fzjKUNHFNjMJegv7//t+YRi4uLa7hCh0dIKTfc7iaPUFjNIy4Vl01O/9t/+2888sgj7Nixg6mpqYu/4DJgGEaXlHTQKaFv9Nhq9Pb2bsjWQRHcSGStSr6zXc/zCIKAwcHBLvNf/XgulyOTWSuA03WlZXEcZ90+rSa4Q0Pr3dE7+5TJZEgm17YdOttdTc42wsDAwLq/maa6o0wmk8Ria9vdne1e7Bj29fWd9xjGYrHzHkNN0zbcbudY9PT0bFg5BUVwL3QMN9quXfaZWG6x1NAZ27JWXNfZJ9u28ZqC5587wvBwglRqrVdmp+0WhJLAD4nFLK66eoDJiSJvvDZF/0AS29Z57dVJZqfL3HvfbuoND13XyeXOL+hLp9NwTutf01bOl9XnGMAYgiUXDuU1XqpkyEYEo+Uatm1SKjZXHxSQSjLQ8kI8obNcaKEJsO0IRUPHsGC0T/B+U5JK6tRDdcHr7G8QSl6bl+wYFBi6uvjFepMkYxaet/b7sXSwLIN4f5ogIsl1ByFWvptcLrvq/0HOS95a1rkrKtjVH2e66tAXVbZSI3GImjp9McGLFZM/3Jej5sFPJiU9UvD5QbAMwRVZ8MM0udzalquur5wvnfNQ0wQPfuUqnn3qCLVmwNRig6mfnaavJ8qX7tzGW29McPh0GYkgbYnu7wCgUvHYd3Ufz09KxrMQiUSwrJWprSHPZbZUIAx8vNAil8sRNAXf/8dXaTb9dqNYVQubLZ/v/uN7/D//9c1MThRp1jzVjjZ16oEgZugIITCkZGnJJcylSLaDClYfQysSIQ5UfcGpQsjVmTjRaPScc0md32dqOq8VMtwysnZaHWBnGp5ZznD/dsFNUtm/CyEQms50ocF8yaMcWCTiBnFbo9oK0HSddyZKbOuNMrDB+e2Gkl/NSL44niZurj32c+WArAmgEYmYHHhgD0l/ipnT75EwdWUbVDhDT273OlGMEIJxO8CuwOnpOqm0w+3jWRouTJ2tUikukck69PTlmKyV0S2NpCUZzUTYMaAms1uJ0e72+gYC3pqoIAScnK+yeyBGLhflruxKha5zDE3L6v42Wr7S6p4tgWOp4AN9KIeUITVh8MN/+oB7v7wTP9Q4cSzPwkKNVCrCzbdt4eAHc1QrLk5UJUSVyy1eeeksB+7fTbXWQgiNVNomk47y1BOHmZstk81FEQKOHFniltu34ERM8CSVSpN6XXWHEkmb0dE0UkoWFup4rk8yFcGJmjSbLs2mR6Oh9Lhuy6dSdalVXVJph2jEoFhoEIaSIFRpUldf20/L81cd+5X2dDa7ch6ee67F43GahQjbegRb4iqOeaqpsyUCQymLK3I5kraa8teEki8ZhkbZlZxYztJEUg7gvQr0RAThss6OlCCdjBOGG5/fhmFedJ29Nh3ieT710CBti253pScV614Pq2FANQTdtckG4W/FI0zTXMcVOvsrhNhwu5s8Yu3+nrvNC+GyyOnLL7/M22+/zZNPPrnusUwms45pFwqFDU/680EIse7ifSmPAd0S+EbQdX3Nhenc7ZqmiW3bG26/82Vd7naBC+6vYRjn9UrTNO2Cr/1dbfdCx/BC273Yd/NRH8PRrMnEcouWL0EzNtTP+a2Qk8fzFAp1tm3L4kTNdT6mnUj1aMxkaDiJ5wW8/+4c194wTKXU5PSpGtu25bjyqgHeenOKBx7aix+utJY2woX87zRN7xLVfFMyV5ccLUBfVA2zDMUt5huC/GyexbLPyOgq3ZyS+6EJEKEkG9MY7FXZ4oeKgnTU4mRJkosKhpISwxAkEXx1u4ahKW3c8xOS2Rr82Z4Vvd3xpsGBB/fyk6eOdm2afCkRhs59D+zBdkyeOhvy6HaxLkbz3OOwNSN5bUEtSjcPGvzvIxqlMtw9LvjNrOTAoCBjC+YbGkdKGvt6BF/fKXl9AX5wVvLNndATgYWGiXGeKerVxxCgp8/gG99cO7yT60+Qz9c5eqaC1R6ek106qZDNOhRdnVr7pkm1E9WFRCIpeCFjts4XegV/e0xyx7AFM0s0m76qYElVDQ6kiuJs1j0W5ivMzlSo19utTyGIJyPUfKVpNTRBs9okOZjlydMBV+VgPGm0raEkUzWYrqpzI6pDI9RInjPUEUjJmwuSEyXBt6+w1oUvAFS8zu91rTbuxHyV2WITQYipa4z2RNneF6VY93h3soIk5PRinUBG6YlbxNrRYRMVyVuLStucXmWG3tNn8Og393P0bAVRX/E0jdsupX/6/+CGont8NrIN0oDJmTo/e/IIdrt0n8k6XHnVAC+9eIZiKyTZsdLSBXd9eReGLrhyMLZm2GX1GmHbcMdeh9dOK0/k4/N1Eo6BbWhEbYPRdqCDG0jmGhAzDObqyvZse0rwyE664R1CWMjQJxoP+cKd23jrzWl27OrltVcmMUyN48c8BODETB59bB/P/+R4dz8WF6r85sXT3HHXDmJxk4hl8tQTh7j+hmHq9T40XSOdjtBs+rzxyhQ33DRKudIiHrNIZyLohobnKS9czw+IxQyIW4RhSH6pimkZFJabNBoe0ZhJJuMQi5m0Wj6zM2WqNZd0KoJhaPziuRPc/oWtSKGf53clLrimNUKNYmByd1adTzcOquCMW6IwGNNR46bryUTSElw3sPqaIjlTlryxCAlTEqJhGOe/Ll9sndW8Ku5b32Po5sfWnFf7RtMUGz7zpSb5qosA8vWQN8+WuX5L8rfiEefjCh/3NbCDTyOPuFRcFjl9/PHHyefz3HnnnQBddnzzzTfzL/7Fv1hHWj/44AP2799/OW+xiU1cFNVWQMTUsW0LU1s/oiSEoLBcZ3m5TqnUwomavPXGdNfcezVBjcUtvvK1q3j7zWmuu3GEK67q50ff/wDbVkNRx464uG7AfQ/uIZ6wLjJBfWl4Z0lFrO5Jd4ZxVlWK4zBRcfCaLk7cxrQMPFeZaHeepbcjHAcHE9S9kDcXJN/eJSi22gQokExUYCxB149wqaE8A/dmV9+VSxYbksRIdJ1NU09/gmhUpxVKHAPOcw1Zg2xEtfUqriRhCbYl1RDKzyaVpdJbi5K7R9QA2MvzKzGntw7AYkPS8CXbUpfn+7rR8M6hfEgs6pB0dKQf4uiS1RVfy9LJ9CX46XzIY9vXXqyLLckTZyQZA3wNdiUFX4kKDpXAXlppSUnaOk6hbhyCIGRhvkauZ6USFEqpdJNZBzcUeIEklnQotiSZiDoPThdDAkAKjd6IYFsSburf2Bge4GxFWUg9tvP8z0lbcNeqyNNCzWUi36Dc8BCEbMlFiFo6fQl1EUlHTT6/K8PbE2UqzYDJpRpT+QY9SZud/TH6ovDAuFgXSRsCRkTjsJHiWzesDE7JBeWbGTeV7jpubZw21GoEPP74EWyCrp3DlVcN8MxTR/DcgHjawZMSIaHR8nn26cM89s39F53C1oTghvEUH0xXKNZ9yvWg/Z1pmLpgIBXhZAmKrjLtT5qCh7aufD6tvW/zcxWWl+vs2JEll4ty7fUj6Do89NUrKJWa1KoumZyDDCUHP5jj+huG2bO3j0qlyehYWjkPmBpeM+CVF89y/Y2j/OSZo1gRg6GhJFvGt5ARDrouOHV6mV27eqjXPU6cWMY0NXp7YziOhiYEumlQKDSoVl3CQHLo4ASf+/xWtu/KcroKlvSYnyuytFijfyDBrp29vP32FC/+6swluj9sDCnVsONqX2dLF3xlKzx9VnLfFuUxfWlQVf6RuOR4Sb3e0CTX9HQsoy5zcn/5DPkX/heZnTeuOa8MXaMnbtETV+f3maU6k/k6TS/kpZNFrhqKk4ufn0Bt4pOByyKn//7f/3v+3b/7d91/z83N8Qd/8Af8+Mc/JgxD/vIv/5Lvfe97PPzww7zyyiu88MIL/OM//uNHvtOb+OxCIPBagnhLUl0sM+kGXcP4ztKrCZiYq5Nst/JffXWSW28d443Xp7j3vl3Uax6lUpOe3ig7d/awXKhz5nSBhh9y43XDHHhgN9WKS6nUJJWKEE/a9PXH8U2dY8uKCjsGbE1e3mIKyjh9vi55dMdaI+sOEiaMjyT4taZz5OBsd8jHcztDUYJYzOpmi//j0ZD9OfjFlKSnfZG4rlfw/KTkz/eqf1dcyVNnJbqmPCI7OFGCsbiyIWKD6ewQeD+vrIrmarD1IsOvpia4KgffP6ne+4Y+ePw03Dsm+OWUGl36YFlyVRY+P7j2s1+RFfzDcckVGbihX1nYXA46U/wAY0nQhMFDD+3d0Lj/wAO7mQt09vWs9QNtBpJnJiT3jQmaruBoW1aVsODKDBR6Vqoza+huu6wdi1vYjollGbiur6qrYYjbUrGeiajJ1pEEpgNLVZ/JoEG1oaqssYhBSpgYoUnT1QmlxNA1ml7A8bkqbiAZzkQYTkXotUIm8y6zpRa2oZGIGFiGRiZmEreNro1WsyUp1j2mlhsgAzQBW7KRrvXOamhCsG8kwXShyVShhR8GLJSaLFVa6JrA1DW29UbJxFYu6qGUPDuhbjyCtp57ZQJcoguohUpOomtiTfVUCMH8XAVHBF2SHYtbVCut7s2j9AOq6NgiACS+G1JdrtOTTm44wLMahia4ZjRJueFTbvpMLjdo+SEnF2pELZ292U4la+2JpgH5hUb3vAlDyehohtOnlti9t59SsYETNdFNoXxgoyaGqbNte5blfIPevhg7d+WoVltYtkEyYbO4WOP0qQLNls+BB/ZQqTSpVlwadZ9E0iaZtLFbBuVSk2jUor8/huOYxBNme2jUp1L28f2QWrVFLG5x9z07icUNSq2A30xJHtqqrLYKyw3eeXua73/3PQAcx+Qb53F/ODcRbjUkkrcX4XRJsj2lfp+rkY0I7h1TEab3b7m8H6uhCfZm1PsuNyVHCpLnJsAxJPeOiQ29VM+F5dco/+o7eOHaqnyIZLqqZEQCwWRVUpIRDNPF83w0KZlcbm6S008BLoucplIpUqmVskZn8qqjXfjLv/xL/vN//s/8p//0nxgeHua///f/zp49ez7C3d3EZxkCwexckx/86CCuGxK1dA62PRAPPLCHXJ/DB8uS0xXJrriF5utEbIPFhRqvvTLBjTePUq95tFyfLeNptmzNEIkaLBeUUH1mosg7Aq69dhjLbrZN0S36+mIgJH6lRX/UZrkp6bmI0bdsz5GvbrsuNJSF092jGxPTDhIxnUe+uocf/fgI/gfz3HP/Hlq1FpVyk9EtGYZHkugRnX86GWJrKso0BG4bVK8fbru9dDRg3z/ZTm4aFl3Lqtmaqt6uvrCsJngAFU/y3pIiYr+clvRFL57dflVWMF+XHC5IdqXVxX6mpqqBL85Kjhclr8+rCM6rVl3wdqQEg1HJd08on9ZtScm2JCTsy7dt6exjbJVx//JyAyPu4GTjvNnSka2QO4bXfpZONbvHEcz7qur3+rzk6j7IWJLoaAonYrbjOlcIqgScqElvX5yf/+Jkt0LfqCvbMs8NkSbcfOsWWk2PhabLqYUaAokm1KBbvelSb3rMFNYfX9F26Z9YqnN2qd49owQhnieoNtTwwZlFNXgjpVwz/CcI6YmbbOuNEr1A1dHUNcZ7ooz3RJkttTixUCMIlc7Z9wXvT5W5aVuGiKm28YtpyVhCdG2sAFg+TenU2zQUN8fSlVtA1pbd6umRYAu2oVNZbhBK5fsrEDiOSbG4MiDj+SG6Iboxr4YuKBSabBWpi5LTDpKOQdIxGEzZvHi8gAxD3p8sc+O2THeSezVajaBLTH0/JBo1WZyv8upLk+y9YoBf//IM8YRFxDHYsbOH996d45rrhghDydbtWWo1l3y+jmHqWKbBs08fpVhscOCB3RiGpmJKTZ1du3vJ5hxmZyqEoWRgKEFhuc7MTIlsNopp6pTLLmEQUq95uF6AbRvs3N2DaeoIHXzgUFsW9OSZkBv7La7aP0Am6zA0nCKXizI8kiIaX0tMV1eGz02EUwm/kl9Nq5uPr2wT57Ud63MEiw3J02dDtiSUnd2FQg1WI9+UnC6rqulIXCCR/N9HJM+elezJwJVZ5WZS82G+JvGkIN9QumFTgxuD09TPvI3G2qr82wvqNTFThWFIqSKGe8wIJ+ernDsPsIlPLn6rhKiRkZFuOhTAjTfeuMaY/9MAy7K6U+Wb+GSj0ZA8/eRh5X+oad3MdtcNeOapw3z5kX28s6Tx2A6BbCV4/rnj3HP/bp5/+iiLizWefvIIiaRNOhPliqsGsB2dYFV2e7Pp89br07zx6hRDw0myuSjVSour9w/y/HPH+KM/u4G46ZNLX/xn0/RV20qgBlIMXVUo7x4Vl5QQ1ZOJ8Adfv4K52QqlUoP+oST7rx1Emhq/mAqYr4dsTSqPS1ODL42t6EjfWVSm+IYmeHlOYmoq6nEsoR4PpeTXs5Ivj57/wrPYkDx5RnLHsNKLXp0VvDQrub6Xi4YHeIHyddWF4NEd8PhpyY3bVJv/+yfV5O6VG0jRY6bGQ+MhL80qz9WfTIEuQvbl1JTvbE2lUV0qVrf8rcEMppDETUlPAAlzhZjU/ZAXpqHuCz4/pCZxi75Ow+ljKCF4YVqSjUhu7Df4xrf2872/f5d6w0O22Wk0avL1b+zjvXdmqBabHDk4x5fu3Umt5lIuNRkZTWFaOq+9MUW50uKhR65CEDKQtBjLOZi6YL7sUmv5LNe89k2FwA8kQkAios63uhvgBbJLiDNtJ3rL0Gh5IcWGT9i2/OqcDRIYzUbY1uNc1ho3mLLpiZtUmgEL5RZzZRcNeON0kVzcYltfjOt7RTvJS8FvVsk//x2CUKWhgarONYSk0IKEJVl4/i9J3Pn/YqZiEo8qGyUAx9Sp1FoMj6Q6dqrouoYQqoIfMTRMXSOTcS6ZmK45F6Ts0pKOJGM1qp7SPvdWKixWfaI6FItNBoeTLOVrxOIW8/Nl7rx7O3//N2/TbPpMnCly861jLMxX6R9IUK+5tFq+GtJq+vzgJ++TX6phWcpPt6c3Rjbn8PgPD1Ms1NkynuH2L4wTi9mUig1S6Qgjo2kKyw2KxQbpTJS5uRrL+TpDw0kSSZtIxMANw+5n+SAvuXNYDRT+dCokN6Cx44pedrc9eMMw7BLTQEpMIdZUhjtYfYP/63YE8h3DorvGng9/ukej4UtenlP6+c8NcdEI6VJL8vyk5MtjK+ePQCgyvFVwtAivzqvvxA2UPAlU9bbuQyyssfj4d6i01PfoBpLiL7+De99/4Uw5Sn8MfjUNtwyIdkS04I3TDTRCBOqm/d3JMhFTJxc3SbY7DxfDJlf4ePFbkdN/DjjXQmoTn0xIYGq6TL2hqvV+KKn76uItJbRaAVMzVcZSKc5UYFdK55bbtvD8C2e478E9VCstSiVlwzS6LUdP1uou2rajc9+De/jR9z/oVpxm2l6G9963m1deOkuj4bO0UKXZtCm0UozFw3UDQqvhGIKvb7/88+rctmIsbuEbBkdPFfnS3dtoxKLUPFVVOFpUB2Z3Zq3+sOIqMlr1JMcK6uL+4PjK4vvqvKompDZonwVSkm/CVEWRoG1JwVxNmV4j1XT9N3ee/3NJVDJSxzdVE4LBGPzjcckD44L9OXhjEX54UhIzVWV3qgpfHhMMxQQ9jsberEoQum+LwAtgsqaOy/7eyz6cap+kpK9b6RaY51yHqp7G3phPZanK/KEGbtbBScbZktLxJdy3BSYq8P0TIXcMxfiX//ompiZLLC4qL9zRkRRvvj7JmdMFEDA/X+X48TyxhE0m7bBtZw9PPX2k68laXKoRsXW290W7MYsjmY1L8VKuTJlLKSk3fRxTkZ3VEY2hlCxWXEKpNIFOmxB5viTpfLhl3tQ1sjGNbMxkMO1xfF5FRC5VWixWWjiWzva+GNl2q98qnoGpt4maa88Px1A0ueaBfuotem+fYTIYJtOjzOZdN6DhBTQKAU7MxLR0XE8Z8HthSNTU0bVOSlT8Q5HT+bKqLocIbtqapuQK6r7El/DmgsTS4e5RndNzDVK2wG3bVRWX62zfnuPd6gyF5SYzjTL/6l/fzOxMmfxSHcvWGRlOcebMMvG4jWHopFI25VKLfdcMEo/b9PXF0Q3BL54/QbHYUOvSc8dZWqrxwi9Oc831w0RjJoWlBuVSi56eGLatMzNdxrZ19u8fpN5U+++eoxsdicO7S5I+R1BswpuLgi8MBetS7EIkP52UXB0PeOpHhwn9AEtjzQ3+s08d4eHH9jFXF3x9+6V3LDpJdft7BK/Pq57RQ+MrN8sdtALJcxPq5vTA2Nr1x2+vuzFThUsorF9n0jZYC2dYmH2HTET5RTcDqB1/m9Hbz3DnyBUIIcjYa1/fk7CZzCuP6WorbI9HBsyWWggB140luzeB58MmV/h48Zknp57nsbS0RE9PzwUn6jbx+8VsyWV2oYbfLleFKF2iQMUmRgyolBqk+tNdvWSsx+HGO3dCqYoTNRge7SOdjfL8vODerOy23ENgYDDO/Q/tZeJssas1daImr7x0loV55c+Yz9exHYPtGcHv6kxZ3VZshZLicgtLd5ESnnjyKHd95WryTQ3HgG1JmK/DaGLtNk6VJY/tEHz3hGRLQkWbdlBxJRMV2U7IWYvlpuTHpyX9UZX2M97ebggcK6hhpucnJaGU3YnmczFVVSlF1qrq8M39glagIlNv7td4fTEka8N8A/ZkVHXjuQnJH+9WerSdacGbi5IfnlR6xp0piF/gRuC3gQaYlQYvP3WE2YrfvqhBPdT4ykO7ONaKsCVhMJ4UDMXhqTMhD47r7Lyil/ycxDXg1UrIvqv6mThbxPUCfF9daH0v4Jrrh3nhpbPdSpcuBNVykyuv6buk/O/VF0MhBCln4zNPE4L+5PoJ2vM8/bKRckxuGE8xVWhyYkFJC1puyAdTZVKOyUDEI/rL/x/n+0iOoXx9NaD26++w977/hBM3+NpXruSHPz6I6wZoQvDuu7Pc9+AeXnrxDH4QEm1P4lmWco9wHIH8EK3Z1Q2CdydKDGQcZtwIZVeRpYe3qup8pj3N7/uqfDs1WeLA/Xtw3YCIbfD2m9O8+MIZduzsYXxrlumzRdxWQLPhUa242LYBEo4dWeDaG4bJ9UT52796m5npcvf9X335LF++fzf1uqtuhqUkHrOQEiqVJpMTRfr74wwNJzl0dInXX5ng81/cTiKlce5I05VZFZhR9mA0IbB0qHoqmnjN50dw3xaN0yfLiLa3ZdkFW5dEDPW46wZMTle4fjB92cc3lKoz9OC4xstzkv91eOX3DIocf++EZDwJtw+uP0nqPgxegsvQ6lSrsO0VHUrVPfJ+8x16v/pfWAhinClD2QvJN5R7hR9GKPkmWuhBqPKUByIhjmyhSRVIcTFyuskVPl585slpGIY0Go2PZAp7E787NN2ATMYBBKYuiJwzPt4MJL1ZB9OAY0XJjjQg4ZWCRp+TYrhHozcNpVZIw5MEoWB1Cp0nJZVKizffmCQSMTl2xKVcXmvGnMtFVbSpzrrKxIfBii9gu7XVHhLptNsMwJEBfjNE1wSGCLGqVa7py+L5AQeXVRVzo6ltiWqtuyH0x9b+fTDKuslrULGX+3sEV+fgeyclXxoRTFSVTnQopiJZOy328+FEUeJsIGvcmYJnJyTX9sJQDHwJI3E1wQ8wGocnz0iEkNw7qjLrX51TRHqyqmJOB2Jiw+GN3warbwakXNEIB37A448f5pFvXstzE6rqa2mCfTl45qzk4a0BfREYTwpenoNJve14MF9lYa6CrmnEEhavvTlDabmO1raSckydwb44yYj+kX6OjwsjmQj9SYv5stslqZVGi/29Jo19XyV23dfO2yK1PfB8H1NAImaApREbjPCnf3wt8/NVysUmmYzD4HCCrVvTzM9VKRQaZDIO/QPxD01MAbIxRSY0Qjw/ZGKxRl8qYEdvrFtlk6skPm4rUM4DEn79wike+9Z+fvGzE3zhi9v41S9PceL4EqdPLZNM2nxwcJ4D9+/h0MF5piaKuG7AgQf2oGsaZ08X1hBTgMWFGj997hiGqXHvgd3IUHL2dIGWG5DOROgfSFCtuiyeLhBNRLj27j3kcsY6YgowEO20rqHPgXNG9dZAE1ApNLp694guqfuq0wJqLcovN7hxR4bL0WYeL0qqnjLCB/VbHY7BXx9dRVAlfGXreju6Dvy2tvlC+y+lpLFwivnjb6sZRKluelzFNZk/9jZy/gzvcgW2Lqh6MBYX3JkFENR8gaMbeKF6h1MLVRbbX415CVKrTa7w8eIzT0438enAQMpiGV3pt8JzFk4BmmUwOpIgGoWpquBUCbanVItovgGFRsCv63B9r6AVrq2kgLICGmnnVs8V1iZuADiOQU9fnFjcvMTFSfLavJpwz9i0s9vXvmnZhVfmZXsgBAbiOvX5BlVfYklJpdTAa6fAaJpqKS0tVBmPmSSSEWbrWrtlugI/VFP5Rwrq37qAkdjKc2wdSuuT5/BCydGi5J5RwTuL6jV9juB7J0LuHhEkLfjNrOT28bXvp+sahqYMuP0g4EwF/mT3+oW+P6p0iFMVyU39gl9PS9xAWWCZGpwsqUn+iA4/Pq20Z3eOaCw2oV7zmT9bZrLaoK/Hob9fDW8ESF6ahSuykLmECd9zseZmQLQvom2hZiih2QpYmK/SsNIsNFTrdHtKcHBZcrZC1/z+1gF4Z1EibY3RrWmGh5M88cNDyvqnpXxRbV3DaufEf9jW9CcFpq4xkokQhJJCzaPUCHl5KiA093PlcIJ0dONJ6GdPBqRlkYgeMlbV2GIrGUgsJti+PYloDzp1js34tgRbRbL7tw9LTAEips71W5Lkqy5n800gZL7UpCduYWgr+2s7Ogfu38MzTx6mXGoCkiOHFzFNna88chXL+TpfeeRKKhWX/JIy4h8eSVEqNRgcSrJzVw/9/QlicZNnnjzCzt296/yVAXRN0Gz41GsuT/zoEMmkTTRmUa+pm+Iv37+b2z4/TsP1+MHJgJKQawYIz4/zPyeUK5Vh9UxBrM0AAilphRBPORji8o7zZFV1YlYPeY4lBDvTyvpse0rdgCcuMCCfjajhqvPtvx9KDs7VGPzF/4UGJNq/91agSHW8LSXRXv4OB9qT+3Ufnjgt2ZYS2PrKoKStqwjcxXILQchQ2iYT3ayEftKwSU438amAY2ostxrc98Aenn3qSNdUXUq1OH357u1Ui3VMEeWGPp3nJ0O2pWA8CW8tSh7ZDj8+CU9UJduTgkPLcPU5ASROXOOxb1/Dd//uHarVlfg1x1GDMJatbWjJsjEEW5OSo0WoeRI/VMk9lqaGfQxNEcfOBccLwdAl0ZyDJaBSbHZTmzRNEARqYhsJhXyDZ548yucP7EEjuqaiognV4vKl2va21NrF/lQZRmNr/7bUUN6eN/QJ+hx49qzksZ1qQEFKZcqftjUeGF95jQHUqj5TkyWWFmv09MYYHk2xr8fYsLWrCUEgJX1RQdwU3D8uOFyQvDAtubkfdqY1dqYFf3cs5JYBwY9PK2mCUW7w4pOHqVZdPF+lesViFnfev4ejoUPcgEJLVcuLLYmhwV0j5/cAXbtPUFhuIKWk1QowfImLwDR1LF0ZyBcLDXJbMrwyF/LwVrXNO0cEP5uUbEmszM1f09bJSSnRTcHnv7iNZ586guEHeAG0gpCoY/xWrelPGrbkHLbkHOZKLY7M1QDB2aUGyVETP1RawGS7UjZVleC1mKiEbE9KBlJrNbarSemF/vbbIBExSEQMko7Je1MVNCSHpsv0JGx2DcTRNUEI5PocHvv2NUyeLXLm9DKJhJL4fPfv3yUaNbnrnh3Uay32XtHHy785y0+fPQbA1m1ZkFF6e2PMTJU4farA8nJjnb+yEIJo3OILN47yyktnAbrJUh3EExah9JFS8uC44NkJSbEFnxvkks7tjbCmMuyuXcV0ITBMjdHhxLob94vhrpGNK+URXWlCLwetQGKvq2JKXpiR3KGdYerM22skC5pgjcPI6sn9qCH44ogiyLvS6vFSw+PMYr3r+TuSsdnRd+mpRZv4+LBJTjfxqYAQgv6kyVShxUNfv4pasUGl0ETTBJGoyUuvTtAqNbtTp1uTESbKki1JjbG40kJqmtJNdojhuXD9ADsZ8i/+4mZmpkvkF2tkc1EGh5I4UQPT0i6RmKoBFYFKPBqLK4H+VE2RRl1rt7HWvb+kbzCBpYtudVbXO8QUIhGDiGNSKDQRAn79k6MMfnMf5qo++lwdbA0OL0tu2rN+kT9elPQ70KlQdEznHxhXldLjxZCxBMQMeGZCDYosNJQ5fOc1BjA7XePv/+ZtGs2VMqwTMfnmH13LQjO2agBpBVVPteE62JsRtHzlcWjpIXuzgs8NCN5aUl6oi6WAn/z4EEuLtS5RDiXUqi6/eOowX/nWNSz4GkEIO1JKW3s+94ENISGRjLC83MBvV2CabZlFLGljGoJ0xmEZSdmF6ZpkOCZImOrmourJbsVmNToE59Fv7mNyuszCYo10OsL4WIpETPtnQUxXYyBlk6+5LFY8yg0PNwh55qwgQElI5urKgzdn+uQSELd17EuYjv5dIRszuWIoxkyxRbHus9Qe7ur4v4aAiOj09EbRdEG52GRutsLV+wZxoibPPnmEIAhxHJPBoSSplNPVqW/dnqVSbvLKSxNdUvrKS2e7/sqVSpPxrVmGhpN8/x/f6+rZV8OJmoyMpGlbG2NqgofGVcX++QnJzjTrImsvFbaj1seNpvXvObCbfKiTu4D/6eWg5EoWG7D3kiq+EDfV+rVllYa+5kt+NiW5NVun8cx32pHBK9szO5YUq+QAq31Pc5akp11lnch3rNhCNAF9CYutG3j+buKTgc88OTUMg1wud1mxWpv4/aA/aXNktkalWWFPb4qTRxYpl1tM5luk7RVR/7NPHeGhx67m8SmNkbhkbxbeXVKE1NZZ1wrvQNN03lwQzLpw81APN13ZT8sLcYy1liwXwnIj5KV55ZFp6YqYtgI1ERwxIW0pPda2NGzUwjI0g3sO7OL7//AejY5dEYqYdpwDxrZksGyDes1lfq7C6LZM9yLiK0tI+qOsG1pyQ8g3oerDTQOq8vzmoho66nMEVU/y4ixclVUVVkPAUFLQPOeD16r+OmIK0Gh6/MPfvs2//De3qAm1c9DjQKG5ok0DuKZXY3+vZK6urKJeWpJclROcqUDUKzPf9gMFpSvTARmGLMxXKcyW2TqWwrLXD4pcCpp1HxlKLEvHb/jdy1soJeVSi8HBGLFcgrwrsTWYKMNwO8lmWxIOLcNN/Rtvu2NjJZMWjqXRCAPisX8eFdONkImaLFbU+XB0tsptfQ6HF12igeD2Xh1L13h/ykUT8hPRQu1L2PQlbI7MVpkru0zm68QjRjdVqBkIlpcb1Gser786iWXrHD+2SKnYJBIx+PafXMfzzx1jeqpELGZ10+Tef2+Wu+7ZwQ23jPL6KxNdUjo7U2FoOMEVV/UhQ4nrutzz5V1dW6oOHMfgm9++hoijr5MPXZkVjMbhiTNKA25dblIFa2+cVifC9Q8kONHSGdQDJk9t7H96uYgaHR3sxeGHklawdiiqFUieOi25tlcwFpPM73uE8RseWVNYCCTQkuQiq+rJAiwtZKrc4uhsBYTqsCn/AGUndcu29GXfIG1yhY8Xn/mjrOv6mmCBTXyykYjouIFgcb7CzExZtfZRU8AduG7AzEyVwXiKn08rHWW+KbmuV/l+3jWy8aLuSkFoRrkpCx8sBRxdDrgyJ1a1by+OhabyHDzXpml/j2rdLzRgoa6iKxOmIoZpm24rSwqBEJL7HtjdbvU1u23FjnPA1fsGmZstIVFt5y0i025tSV6eVUMO3x5fv8+LDWXFUmypYYLpmoo5/dN2TsbPpyS5CPhS8MGy5PpewVSVNdZLuq4xNVlaR0w7aNQ9ZqdK7L6qlyBYe0lbaoC9wYojEAxGVZVtZ0olV31+WOP4G9WutVcnvjUIwm6lZGG+wsETy1x19SAy6dB7CSEB3fds603ff0+lcD315FGV6tSGZel86d5dHPQM8o2Quq+O11JDmfSnbeWUcCGcXKgxXWigIYl/iDCBTxMy7YEjQUil7lKqe+goScuxWnvgr/1cx/rkHIukYzBXdtf9PW2Dm4zw61+e5pbPbcF1A4qFBvG4jRM1OXp4nrvv2ck//O07ys+23ZL3/JB43AIpue/BvSzMV4lEdEZG+0kmLcqlJlJKXC8kkbDW2FLlelSXxjAEuiE2vBlOWoIb+xRB/TBWdbBx5K+Qkthynb9/4ghRsfK7Xe1/erkEdSgqOFKU6+RTGyHfUh7GliZo+MpzdqYm+dygoN+B703GuHXrF5ARsa7jdHBWsjtDNyEPoAqUGlV1dZBh99yLWhq7B2IfqnK/yRU+XnzmyWkQBNTrdaLRKLp+CQHim/i9Yu9QnCOzdYpzK+kyqpO7sjD5UrKUb3D3rgyvzgWAqjjoQlVXDy5Lrjyn1RRKya+nA3bHXUaSEXocZTp/qKCGX67vuzTisydzvueowZ/hmPrv2l5BsSU5VoRjpfZTpCTlSAYNg1dePkGj4ZHNRjlyaKF78YtEDKIxk1YzwDA10hmHUCrT/J9NKVucrUmxTrcVSsmbC5KooUyyax68OCv5g50CTQgmKkqvaWmQjcB0DQYcRVj/cNfKtgxNsLRY40JYXqphaH0EG1xd7Yv8xBKW4NpelQa0kLS77cW1xLT93ESEQrnFs08f4b6v7+PpMxo702pA6kIJXLCiNy0sNzh0cJ77H9xDtdKkVGySTkeIJWzKlSYiEWMkoeQHEzV4aU5yTa9yF3h+Up0bCw3I2sosXKLeOwhlm5iqS3rvhaZB/hnAMXVu3pai0vQ5NFND61S7Ub/Rzgzjrv4og6n1lle/D7T8kOPzdUAQsfRu1RTUzZtpadx48yjPPHmEZNKmpzfK9GSRfL7OPQd2c+iDOf7oT68jn69TKNTJ5WL0D8RpNDzKpRbvvDVNPGFz7fXDTJwtMDKSQkr4+fMnue32LeiaRqulvIxT6QiaJjBNDecCuvalhkp288Lz6TMvHZ1EOImkWA55/PEjhH6Aa6z8fjqdqEfPkQ9dDH7YDvoYu7T963cEtKushwvqRvULQ6rWCfDA+Pm1tqNxeGVOaXNXo+Upc+behMlQOoJtaBdMSLsYNrnCx4vPPDn1fZ/FxUWGh4c3T7hPAVpeSMsPyWQc5bUYcE6bR5l9j/Q5SODGfnWHfEUWfnRK8tVtgrcW4b0lyb6elReeLKk8ctsrE4Ymadvk+j54c1HS56jH912gAjBTk7w4I0naKp2pN8IFTfoBsrbg2uRKjGAy4zCQS7FU8vnyfbt56onDLC3VqFRWiOm99+3m4PtzCE1gWipPu+aFPHlGRQ2+PCsZ2EDff7ykdJJeqOIOX5yTZOyVuMG3FlVF+eU5iYZkR0pVpHWxVh7gh5Ke3gsPEOR6Yl1T7dXHx9IvTho7CEI1vGHbBs2Wv077FokY9PbGOPjBHBEZUlio8rU9aY4XJE+fUf6u1/Se/4K2enJ5caHGsWMHicUtknGLY8cWKZVaPPrY1dzQF/LshIYQKlggCOHtRUncEMRNeD8v2ZkSHC9J3lkEy1D+sBmt2U1p2tEXZeATQsh+l3BMHcfUCQbA9UOyMZNExMALQs7mG2RjVtfS6feN+XKLEwv1djCsYOs553QQSmUHFjO578HdVMourZbH/muH6emNsbRUJZGMUKm2GN2SZOv2DDKU1Goub7w2yaGDC4AiudffMMyrL02w9/9xIy/87CT3PbiH0yeXmZmpMDiYJJWKMDlZYPuOHkzLuKDwo8cRfHWbYK4ueXUevjB0/gn3C6HsShYa6mbqVAmG6xWiIkCaUPPAtGT3t+O6wTr50MXwfl7S6yg7u8vFdb3rP8/5fscSSaml1vwzZblGi6sszQRIPhIpySZX+HjxmSenm/h0oe4GNL2AkZ4EHiqZJL5q3WkGkIrpDA4m1iykcVNw24CyWPr8EPxkQrLdX6mGVj0YOGeKvT+qXvPBssqCjhpKPnDuBDzAUEzw2E7BbE1ysqy8/4otSdKC2weVv1/HYQDgeBHMap3nnjpCyw3aF0mIWDp/8q19/OoXJ/j6Y1eztFijWFQDUNGoxfvvzlIuNbEjqt1mOzo/OB5yYEyQtQV3jqy/VP3oVEjEUPvxTyclu9NQacEdq+QNNU9NvXdMqx9ttwy/vWvttjqWW52MeWBNNdOJmgyPpBDntPQnKpLbBi79IiqlJJ6wuP/BPTz15BFaDa878xCxDQ48sIdmy6fWdlVolhuU3Ax7s7A7C6/Pw/t5uDonN7ywdSaXTVNjOV9HSkml3KJaaaEJsCMGQRBi+aqSvD0peGVekfadKZUrf12vYKIKpytKAvHMhOSRHepi+NKJOgJJPKIzfJ70p3+uOLcyauraJ2oiuuEGHJ5V7gJC07lqOEn6HPIipcSO6MTiFq1WgBMzQEC51MSydUbH0sRiFi03wG2FVMseb789zcxUue0gonSOpq3TbAXcc2A3770zzQ03qUrszbeOEfMls7NlioU6u/b0kkrb/OwnJ/ncF8YvWqVc7W96uXhvSTJTl4zGlW/w9rQgP9vopkUZmqTkQtJaCSpZKx+6MAIpmWvAaFzwo1OSvqiy1BvcQAcPsFRVMpB4xCAZMXAuWN2UnCrDZAWWmpKmD7sygjtHVITqanIqhLrxaPmbvqSfRmyS0018qtCJYpytt7j7/r288rOj+K5afAIp0Uydhx/cs6GIfyShtJ65iOCeUbFmoZyvSyJCMnRO9zVtC24fVMM6c3U1cLTtAkbRgzHRFvWrx48VJd8/KRFIsg4YQqIL2BsLefqpIzRagaqwCdVSF0HAW29McP2Nozz9+GFuuHmUwcE4EcdiaanGzt09pNIRhoaSRGLKmNsPIdduiUVXSQ/8UPLWonIH+PKYslcaigvey0se3Cq6z52oqDGdmZrkjhElZ1huSbL2SlttNWJxg2/90bVqKKqxalq/Pczx5msT7NrTz+BwrKsPW2rC3szG3+n5YFga/QNxHnhoL+VSg+V8o9tyj8VM3npjqvvcgV6Hl2ZDHhhX0o0b+1RcY8WFzw1u/F1FogZ337uT7/3De3jtWFwpwXYM7j2wm9fenMa2dW4Zy/CTsyFJU7kNpGzB/ePw9BmliVtoCBYaygXhvSUYiXiEofrO4xfTMWzi9waJYFd/fB0x7cCwNKyITk9fjFKxiWX6mO0o1dnpCj29MQaTJqGEpfk6SwtK7iKEIJG0aTZ97j2wm1dfOotuCK7eN0QkovHIN65merpEs+kxvi1DTy6GrkOr5TM3U2ZupsLYjkuvUl4IFU/y7qLyMG0FalCzz4F7x0SXeJ4sQy63MrkUMwTltv1dRx7ckQ9dCnQhuK/dzr8qK1huSo6XVBcqZ6tEu7E4gGCu1OT4nNKGyvb+DKQj7OiLbRgV+saC+hzX9UJijXZZoIuQpYakxxE0vYC5YrNtF/XJuTHaxKVjk5xu4lOFRRVnQqXps393irFv7u9OnUaSDrFcglxm4+nSQ8uwJ6MWSV0T7QVSoejCcATeWpJ8IRUSP+eXsUI6Lw+70oJd6bWEUdcE06fLOIQ4G5jHnzy+zDXXDfHVR69maqrE/HyN3j4YG0vTavmYpugSU1BT+G6giG+xpQj4fEOR1hv7NW4b1GjUfeZmK0RmGwxmHHQvgWao43S8pAjzyZIylL99UPDCtOSr2zZup/nA4HCMv/g/bmFyorgyzDGY5PVXJ3j11UnefHOGv/i3t2DHDYotiResHxK7GEIgmbGxIwaz8wYIla9uGhpvvTlNYbkBqKGNocEEr89IJsqC8aSq0Hx5TPDUmXAV0T4HAiqVFvc9sIdKpUWp1CSRjBBPWBz5YJbFpTr55QbjO7JclROcLkumqkqvbGmC2wbhaBGu6YFnJ2B7Es5UJGKVYHDTquaTB0PvDGhJZotN+jaIfQV1/r1f1bgy7pPNOdQqLq4foJs6ud4Ypq3htRnb6gn45eWGWl/GM9SrLQYGE4xvzZDriYGAwJfkclH6+uLI9usNQ2NpqUEQShYXq2zdlcW/lDLlebDUlLwypzToO1JKcx/SIZtrfwuHlkPuOMf/1NIUCbQ02sERiQ9NlrMRwc0R9b5LDUVU4ybgu11iamgQypBQwlyxiW1ojOVWfjsNX/LMWUmPs1aLuhojccFi2w1kIq/WBgHn/X438cnGZ56cCiGwbXvDu7RNfPLQWbBNXUNDoK2aOj24LKkJuSExLbQkC3XJF0cEr87LdS0xN4RbB3WOYfL4GUFfPOS2QbGmEvlRwNAEuia6xOpc1H1JM4B3jxSwtw+yfSzLgOdSLbcoFBvkctE1VeGFhsrGjhoq/tMNlX/oF4cFQTOgUKgwUZT89LmjBCHUAzWU9EFE574H9mKkHfJNQMDWBLw8p+IHPzeoqojn+1nI9tT+e+/O0j+Y4L13Z/mn773ffbzR8JiaLLHrql5emPG5sf+3myymJ8VY0uYnzx6nXGwQaROMzjSx7ehsS6rPvxpfHBFsYEWqti2VdvVnPzlBLG7hOCZnZyqUKy5xE6KmwIw7NHzJzrTgaFEyFFPTxI6h7Ldemw+p+4JHtgmem5Dc0i84shigARFTO2+U5yZ+fzB1jS25CGfzTUoNj6YXEDE3rnDXfYnn10mn0qTb1cUgVOEAq0+1znk6tj2LEynzs+dPcuTQIl//gytxohavvnKWA/ftotkMKdcagMS2DSzbwDAE+aUaS4t1KjWXaNzmVBnGPmTBrxlIfjaphpHSF7khrHiScgvS8bX+p7auJFLC1Lq/r4+iOd7jCCKaT8sLOThdRmtbO10zliRuGxydqzJbcplcbmDqGgMpdW2erELMhM+fh5iC0oJ/c6fAC0IWKy0Ekoj50f3+NrnCx4vPPDm1LIvh4eHf925s4hKhtwdqOgtQb8LuTp16QUjeVZWCc5GxVXLQsSJc27M2as8PJVlb+dhdMdqDWZK8taBsg8YS6za1IZabijQnLS5qBH9ujCC0B7l8VQHNRWBLv8OWPkGzHpCvtKjXPaIbDJPM1WFbW2d1dU6wNana2vmFBs8+dYSbbhnjmaeO0Gr5CCFIpSIYmobnhjz71BFufehq7h7Rmawo0tpJd+pzLvwZOlP7x48tcfzY0obPyS/VaIV92DoM/ZadtfeXAr48ZvP1r+3l5GSFRqkdZbrKh3G5KemPrt3vCzksrE7MqVVdalWXRtCZMhfEozbjo0l+MhHywLhoTwOv3d69Y6rKfMew4Oqc4L0lyd4knG5C0wspN/yuFGUTnxz0Jiwm8k00JMfna1w9klz3nBMlScQ0yWbVJKQfXJyeCSQzM2rAMQxCWq0A09C4/fPbOHWqSK3mcsWVfcTjNo26R73usTBX44VfnOTa60fw3YDBwQTpeMhGhcqJimQkvrF2s+ZLzpRVbPJ9Wy5OTAF+Na1u2DfyP42nHPxEnHSv6tIYhoala0ipnA4+TMb8TLHJyXbwQMfRYbzHId72mNvWG2Wp6uEFASfmq7x4tk4tNAh1k+t7JCfmQ7Ixi1x8vftF1IBmy+PQTAUZqkn9nf0fXUt/kyt8vNhcNTfxqULnQq8RcnimghiCnoRq2+xIqYGfzw1u9EqVDNUKJOcWSTQB9VW2nTtTgvEE/NNJydgleJxKJO/nle/8fF0yGlcm+GlbaRTPbY2fGyPoS9WOt3Tl1ddpoy3N1zdMcun4Dh4rqcSnh7Yq/ezW9vW11VD2L6alU620urGJUkrKpSaZrIMQAtf1KS5W2dqbwdJCXpiRPDh+aZWGS53af3sh5MZeseHFdCOI86TTJCyBrkkMR2f3zjQvz6dxo2A6dCs6QzHBcOzyqhrnJuZIqdqZuZj6eyKmM96SvLsI12wwRWxpSr8MMBJX9lyHpUnK1HE9yXvTFbJRkz2DsUs+Bpv43SNuG6SjBoW6T2ODjM26LzlWlBy4RCukDiSCdNrB90OqlRaFfINE0qbVDLBtnXQqidsKKActjh1Z4FcvnKbV8jlw/x7efH1SnXMJa0NiCvD2kmQwJjaIGJW8NKMm8L+5U5w3aGTtK9TzO7+ZjfxPX5gOGQjAbPmcnamwsFAlmYwwMJggHjexIvolp+aBGkYTKD12IqIzkomssVkzdY3rtiSZWm4yX3YZjPg0Ah+dFuWKoIJkrtjEsXSuGkmuqXgbAhYqLjIM0DXYN5Ig5Xwy3CE2cfn4zJPTVqvF9PQ0w8PD2PamNuWTjp64xXVjSd6fruAFiqCO5gLGe6I4hsALZdsGZuPFea4h2Zdb+5gmBI1A4vse+XyeXC6HaZiYmuy2cC8EgeCO9g11CJwswgd5iaULZuuStCXpcwS9jiKgUQPSq0gRro+pqb93yKeui3XEFFZ8B7/89X2crWg8skNHY4XIdczlXTcgmYpQLK6VD4RS4nkBlm0gJZSLDTSRIWEpY/mzFXlJoQMbTu2vguOYDI6keHkm4HgB+qOS3WnBoWWlG+uPwnBshQhrKFLdsdU6N51G6cw63xfcOiApNtXR7+CKS4xJXHM8WKkYzc1VmJxvEEs57NkSo94s4foGV2UNWpd4BX5wXPDrGY1dQ0nOLlYp1z0WKi4DKYts7J+31+mnCaGU1FoBEo3B9Ho3hV/PSK7KCsLAZzGfR1oJTNMkHTU5uVBjqeoykLIZ74muuekQSNIZh2jUpFpp8cxTR/nDP7kGKQWF5RqtZgBCsLhYpFb3uOueHaRSDpVSgxtvHqV/MI5hnT/xTEoVZ7oegpv6IeD8CXjnorpxjka3EwVw04CgMlfjez8+xOJCtUuabdvgwYf30tcfI9PjnJegFloqwtTWYUtCdC3mBlI2uzfyvENZku3sj7El51BseBRqPrWWTxBK6q7yNWm6kqNzVfaPrpjif3W74L0JdSO+rSf6kRPTTa7w8eIzT0438elD0jG4bizJwZkq1VbAbLHZzcV2DBVpd74Z6VKLDatrG3lZX5kVXEiyFMr1AQAagp1p2NkdghIEUrJQh8UmtHzIt5TN1K50hIcf20dhYW2MYCRqcPZUYR0x7aDpBhTmK2w3Nd55vUZvboXISejqWRsNj5GR9YkmgS/BVs4DqbSDH6o52d1pNUx1qYlYa6b2m17XUsqJmnzzD6+loBl4YchIDCYqkG9ItqcFlpC8MA3X9ynCqkFXhtD5zK1Q4tg6Dz64d8N0Gg1B9kM4NC23JGfKajDu/byygDI0QWhpTDopevemmaxIdMvHrXS8BsRFwwM6iJuC3WmYqAr2jSR5+WSBMAiZK7nKB/S3MAHfxEeH4/N13EC5bpzbIi61JGUXxhIC34diw2cmX8XQ9a5PhyBkejnENjSGMysSnVBCo+ny8Neu5G/+91sszFd5751Zrrl+iLEtGQqFBpNnC0TjFnuu6CMSMVicr1Cve/T1J8j2OLgXsD7y5fnN9y934PBIQV70hk7UfZ48h5iCchZ46onDPPiVKxCWyUtFjWt7BYZQ+zdfV+lQ/Y5gJL4SWaw+m8S+BAJtGVo3ZrYDP5RMLjc4m29Srnu8dGKZnrjFWM5BE4Jq00cA8cjm7+zTjk1yuolPJRxLJ+UYVFtBN/Wjk898vszpUCp7lI3gbfD386c9KSw0lHn9/p61hLfmSwpNpT9NWgJdqEn/zrT/6bKaVp2rw4t5wYFVbTQpVRzS+QamAEI/ZOrMMlP5JosLNTQBmqEqrqdElJ1pdSWoVV3iCRvbNrqtfQC9fWEQhk4sF+enEyF3jwh6HcEtAxf8yF1MViWHlyXb0jH+1b+9hZmpEvmlGrmeGMMjKY62DK6IhHxtm+CJ0+qqlo0oj8W0rVrnJ0owW4e90YCnHz+M9JXfa9lV1VFL+3DpNBdC3Vfk4vlJda4cXpYkLLV/tw8KXpoL+eLwb9d+74/Bu2cl+3o0+pM2s4WAhYrLQsVla4/Dlpxz8Y1s4ncGKSVzpRYSjYRjrksNemVesj2ldJXvTZSYXWySiOto7ahk6NyOhiyUW2vIqZSSTCbK3EyJf/kXN3LsyCKFQpOF+Rq5XAzbMti2I4dpaui6hh8EPP+TE9QbHl+6Z+d516cOooY6h3UhL6ptv8hRYLoK94yd/xmGoXFqqkSz4W0oM2g2feW5bOvcNJLhWEFN2xddMJB8Y7tY18GqtdehD3uTZmiCre1q9eklpetdKAUsVlprLMF+mySoTXwysElON/GpRMsPmS6qC0x/2/S70FKkL5SS2Tr0O2uHkzQheGznxtsbjHHZVikDUcEt/fDGguTdJclAVHBVVmWuLzZUZaLkSobjgut6V9KRtiYFIHl7UVk+rW6jwcYDUx10dKPpVIQTkxWSVjveUwS8+rMjPPrN/Sw24rSEhikDDn4wx4H7d/Ps00dptXw0ITBNHcvU+dKB3bzX0Lk6Bz88LRmOwb6eS/vso3HBaLxzbA12X9WLofXhh5IgCDk5H3JFRulufQkDUZisqsq2oQm+OhxQy1ep5V2qZZ3QDaiHyv5KoCrZNQ9qns+ZqQq7dqXPq8O7HIzEBCPt+FhQFjWmpt7zhRmJH0CxJUlcpgNU0wsIQtUqrrsBu5Mms0WXIJSEiK7u+PRSg/6kdd7p8E387qHWDYWhDVr6uYjk5EKLRqGB6/t4zTrRdIx9W5KEUoU6+KHkvakKzXPuajXANDWiUZuZqSJX7hsgv1RnYb5Kve6xbVsWTRc06h4tN+B7f/8e+aU6dtSkZyhFvhGQOU8FtOJJpFQ3dcWW7GqdPwyW2q32Cw0MWppGPl+/4CBYsdggGrPYFtPItG/yJCoE46mzEltXw4IRXYUSuH7YliF96F0HYEvOIRc3qTYDzuYbNLyAQlVN/iciOqa+6ZLxaccmOd3EpxKVZnvIhxUfu15HcEUGnpuQRPSNJ87PF5+5UOdDWYRkI4J7xwR1X3K0AM9NSh4a19iWBEV5VBv56TOSK3OwM9X5u+CeMfjfhyVzdUVsO5BS0jeQwLTUVH0XAjw3wLR14gmbctUlbYPZ3m/fDVmcq7BlW5ov37+HJ588Qpivc+jgPPc9sIdazSUMJb29UfoGEvxiUSOQ6r2/ulUwW4cXZyRfHOayM7uDICRoqxAkEsdQ3p8PbxXszapowa9sFczVBZFanb//uyMYYcDwQJx43KJcbJBMRTBstU9+++KlAfnlBseKGXamLu2KdqwoeX1BEtXhwJYLD4c4hkoZ+8Ep1eIfjsN8QzJ6CeQ0lJKJfIOJfH2duY0E8nQ+g+z+NW7rm/ZSv0cEoeT0Yh3Z/lYk8MKpKtt7bM7kW7y74FNueFzXK5AyxCBkOGVww3gS2165XC61k8n8IMQLwra1HZQLLebnqpw8scSNN48xM1XGMATXXDtIpeJy6uQyMgwZGEzx5uuTLMxXiSdtHvvmfk67BkFLkundeN8rrnLzaAWSL/0WxBTUTePdF9lGsxmQy0UxLkD00mmlr119Yx+GsDcVMGC4LFRaHJvRMHQN1w+7v5OPwuIpbhvEbYOehMVSxSVfdam0Arb2bHYm/jngM09OLctidHQUw/jMH4pPFez2BV4AJ/MuoWGxOy3Ym1X/gWrzVzxJ4nxGl6uQiSgrqZ6eXvTzLsbnT4aKGoL9vZKyK/jZVMj2pKA/qsjPeFJNcr++oGJNUxaYmqoM7kzDz6ck/VHJTf2iu6/S1rnpS3t44bkjGEGgdLRCoOsaB+7bwUtvzeIYdFNeOujEDI6NRHnoG/twC1Wa5QZ2RGd8Wz8RR8dvyweipmRPbKW6PBpnVTX0w0IyWVHV0rK6fnNzv6povzIv+VJfyN/98AieF+ADharLyEiKIJQUi01SGYd6m+QKoSosibTDqZKquGzfwCbsXFgafGlE4Ev40WlJwtzIhUClZ83WJJoGd48I+h313e1Ki0s4F+D4XJWFcovO+IoANE1dnDuXal1AwjFIRAxilk5Pwtqc2v89otL0CSTtBr3g2GyFxbrkrZkGpiYZcGBfCoSQDKZsdvSlCYPMuuuD2b55k6zYOrUaAYsLNZ558gi6LhgcSlIqNrj2+hHOnlVhFf39cXp6YxQKdSKOyaN/sI+h4STRqEmvwTqf3tUYigluG5C8tghBCL/NPc5GkoBmIPnNrMQL1c3pcL3E2HCSiGNSqbTWVTsjEYNkKkKuJ4YfSBbKLaYKja7uU7Tpf3PN+0I2ap43levDfpaBlM1A6nc7pLTJFT5efOaPshAC09y0m/iko+4GTC43aPmSkUyE00v17mNT1XDDNuzxkqo07L+EVvVSA0Cg6+dvtx5ahpIrufU8GfFae2o/31QDQB8sS/ZmYHtKLaC3Dihta9NXrXhNgBsKEuYKUdqegpv7lfn/7rEoyUf3k1+osJhvsG0wSjJh87OfnaBYaJDcgHR3YgYFkoGMjt2TQVulZ+0k2gDsSUveXISx9RaPHwoVV/Lj06pqes8IPD2hqpD9jiBrw6kyHJ8oE3rKsikEShUXO2Zj2QZuyyf0A1IRo8vuLEtn51iCPRH45bRkqanakY6hqtAbEb3V+drf2in46URIsSXX+D6+taiqsg+c410ahIqkX+xc8AKlNxRtE/EbxpPE2pU1KWU3CWyTiH6ykHIMtvc6LFZcKk2lcR6M0g3lGEypm4eehEWmTaB0bT0L7GgaBbBYaTGYdsgv1SiXmjSbHkEg0XWNs6eLvPTiBFdc3ceNN41RXK5z5PAiY2NJrr1+GMMQvPH6NHv29pHtj2JdxOq+P6ZxHZJfTK9v6y83lUyo4UkSliBuQq9zcd/lDuoeXJER9DhqGOm91xscLVV56KtX8MSP1g5FRSIGDzy8l2yPQzSuKqdnluq4nocG2IYgZhtkYyaaUBVrTQiycRPnUypp2eQKHy8+8+TU8zwKhQKZTGbzxPuEouEGvDVRxm9X1JZrFQAkGpmYxWDO5uV52a6FCFqB5IkzkpYPD2+7NL+/tA0nCy49WoN4PL4hMdmZht/Mwg9OhvQ4KhJwS2I9QcpFBFFDcnVO8NYiTFXh80NqcTY1gXmOo1DGViS65sO2SMCho1WCWoOeXJS+gTiLPRlKdpqpiKQ/DJhbdolu8MtdGzMosLT1etbV6HEEM7WPQMjZxnJTaUYbnsrr/vyQ4JU5yVe2qou/oQlalQaFFqQsKLlqMOrd92e5/wGliw3bOxsAjq2GvCJtO6k7R2C2pgjkZBUqLckNl5A8tb9X8JMJyYExaIXwxqIkbgo+P7j+taamJAijsZBqtXrec8HQVBU7DCQDKbNLTKF9EbtMWcQmPh4IIRjNOoy2Nd2uH2LogrobYGhinRb4fNcHU9foT1rMl1UEZzxiUq95FIuNLoF79eUJDty/m58+d4xD789z8L15kkmbTNZh6/YM9brLu2/NMDyaplRoMDCcoHEJRv/DMcHJEkzXJMMxpX/97glJLgL9UeWBOt+QFFqqe3OpyEZWnbMSsjmHJ398mM/fofHtP7mWuZkKi4s1EglbuYpEdHwvQLc0Ppgs0fRUMppjalw/nvotB7Y+edjkCh8vPvPkNAzVRSiVWm+5s4nfL4JQcmimynLNQyLQdJ2YpVNueGiaYCAVYXtvFCEEPY7k3UXJe8sSDbhjWDAc27iythaS5yclV2ThzXnJFq3O9bEoG5lRmZrgi8Oq+plvwnQVXpuX3DWi9K6rcaYCp0oqM9oP4fHTcMuApOIKhuJrBxE0Ibi5Hxbm6/zgu0fQgwDPC5CBJBo1OPDwlVQjUc6WJV5M59Gv7eH5p49uaM5/uTGDl5qAdTFMViXHSpJreqDkwZGiGjo6acPJkmrHN3xJb84hZSv9nKmpY7mcb/DqO3N8+YE9aAJKxSaxmEVffxzb0vHdEMPSAPWdAozEJW8twIWkFh30OYJbB5WsQNdUdWijz90MJGcqMF2D+0YCmvUGTtTpklMpJYsVl+WaSyiV3nBFT7qJTyM6+t+4vfGl8ELXh539MRbKLiAp1T1icYtUKtKN/F2Yr/LrF07xzT+8htnZCksLNeIJi77+BFHH4OXfnOW9d2e54eZRlvMNDHnpdvZX5eDQskq2e3ZCckOfYNcq+7rfFkr3Hices3jyx0fp7ZvigYf2cOVV/VSrLovzZXK9cWJxi7dOF9Sa3F55tvY6/+yIKWxyhY8bn3lyuolPHkoNj2Ld58xSo61a0rBNnSuHE8RsA9cPMXWxZoBpR1Ly0yn4+jZB7BI0pitYSfjZGhUcm7n4a01NMNBuBe7OwPdOSP5s79okqL0Zwd6MQKJ0XPN1eG0etiaVjVHMkGQjgiuyENEFrUbAT585iu75FIpNwlB5qFaqLt/7u3f4439xA0NDFmlTkrKia2IGO/6oHyb/eqLCJetyN4JEpSfN1lWbsRko0vnkGdXa3pmCV+cl21OCsTj4WhzH1mkFAR1u3QqhWW7w8otn+OKd2zl0cIF6vUWl3MK2De57YA/9A3GSGbv7+TQhLqlq2sGgIyHeQtcEg3F7XWpXqenz1MkWGUNn0Al4/KhLWGtiLraoeh4393i4XogfhAghQa583x+lfm4Tnx4U6157fRLEbJ24Y1GtuEQiJrWaElyfPlVgaalG1DFpNDykgIPvn+D4sSUcx+Ab39xPtdqirz9Go1LB0sE1Lh65qaNM9H8yKbllQDAYvbzf7/mS2FbDjujc//AenvjhIebmqvyv/+uNbuX3plu3gATD1LrEdPdAjJRjbHr5buIjwSY53cQnBi0/5O2zZZpts79GAGcrgpYw6UnHmZkW1LwQS4O7RiS5VdXKHkfjmpzklXnJ3SMfjmhpmkpxWo18U02O958naz5uqmSWH5xUtiljCcG+HHSqFwLB7YOCpYbktQXJyZLkwXGBFyqPz++eUEbYfeUKjbpHsdAAqSQAEjVQ06y7nD6+xM5dOSzNAPR1MYNSyssipmfKktEE3D8u+P4JyZ/vvfxj5oYqxzsXUVPxAkG8Lc/bnoRX5uH2QdW6zzclN/YJjhQN7jqwh18+e4RSI6DmtYc7DJ3bPreFX/z8JPPt7G0hlJfiM08f4YEH92JHjMv2O/WDkJOLdRbLre5F+OxSneFMRBmmuwGVpk/TC+gXEguB15CM2z7FRoO0UcOwDRpNdXRtXekR626AH0gSEZu+xGby02cR+XbEkgQyUZNWzaenN8qBB3bzzJNHqdcVQX3u6WM88PAerrp6gIX5KoNDSa65fpi+vhjNpodhaNgRnYWlkHQixIiDvAh5LLTU4NK9o5cWVdrBxZLYViMEevqifP1b+1mar6qWflxVfk1bTeDnW+oYxGz9dz6QtInPFjbJ6SY+EfBDybuTiphKlEH1ZFXnmmGLa4ciXTPnEEm5JfnZFNzYJ9vtWfXY3qzgdEW2k5s+mraSpcFvZiRNX5I04aZ+QcpWk/MdXJkVXJlVtlHvLMKr83BTv1xTnetxBPdtUTZXp8uSnWllN7UjKcgvt3j/TIFm08fvuHALNVARBBKJZDlfZ3bG4tAH83z+i9tUatIF9KQXQ74JhwqShg8JC06UJDu6NleXhpoHY3GVpHMu9vXCXx2W3N7Wdf5mTnLfmNLXvlV2ePSb+5mfqzAx3yCZcRjKRvjFT46xtFilOyDfHppqNZTZd37JYHAsdcl+tFJK3pkotfO82/6KKNuriaXaypugcrnTMR3XlyQiBp4n0Rohti6JORr9yQiJiEHU0s8bjbuJzxY6dkiJiEG15PLqy5Ncc80Ao1tS/MmfX8/8fIVKuUU2FyUeszEtjV4Ro38oQX6xxonjS2RzMfr6Y4RByNRUhfdmZth3y178IGR+troheax4kg+WlSPF5RLTc5PYYEUS1Eli88MVg/+DhZB80+CWsRTZnEOr6VNuBfTGLayoxunTRQQhvYlN+6ZNfLT4zJNTXddJp9MXnMzdxO8eyzWXuhvSMSDZNRDnth3rK1IagrQt+PKY5EhBtYzHE5Lr+9Rk9Hx9ZRL+cqFpGrFYDG3VdG7CEjw8rojnXB1eX5Dkm0qruTW5kgy11JAIAdtSSrP42jzcMkC7Aiq6+7MvFzJZXSFXrUbAr58/wfadue4wEAASAj9E1wVBCKlUhHy+jm5oH0lq0vV9gnxTJc7YOhwtKhsb6zLsaTK2IHOeYknNVQlZoIYyvEByoqTkDuVWyLLUGN2WoZLOELfg5MEZ5uZrazci1AUVoFRqkkzZavL3Egn52XyjS0xTjsFVw3EAZkstKg0f09BwTI2YrZN0zDU6Od/3KSchmUxuWsd8hnGh60Nn6G085XDi2BJuywOhUS61SKcjbBlXQ0+Nhkel0qSnNwZS8NSPD9Fq+d2JfsvU+NKBXaQyEeamHf7hb97my/fv5a03poH15PG5CdUduhxiCmqtOZeYArju2iS2X81I3FBS86Dhw4Ex0G2NZMRRCu96QGjA6cU6rhcgUJHS/9yxyRU+XvzzP6MuAsMwyGazv+/d+MwjG7MYTPnMlVpoSA5Pl9k7nKQnvnHLNG4KbuhTSSnFFnz/hGRnWtkAnSrLVcMBlw5N04nHzzch1NaZjikd6WQFjhXgtfmQkbggokPVk7QCQc4R7HQCjh2r0Cwpc/lkKsL8XJlozGY8l6Dg6mRtodprhQaxuI0TNbpatQ6kVLYtTtSk0QqYK7rUaj5TMxWyo2magTyv5OBiyK2azt2b+VCb2BClluQ3c5J7xzR0TbRTrCRTVTXAoXwUVWWzNyI5XICtfQ66oYi4EOtvLlKpCE7UJDyHmL6Xl1yV3XjwrdzwEIToAq5d5Zc1dp70rdXYXBc2ARc+D7xAEjF1WlWP/FKdes3DNAWtVki16qLrOo6jk81FmZ4uk19q8MxTRwCl+RRCIqWk1Qr4p+++z5/+ixv4yek8jWqLaqVJJGaSL7nUfJ/HnzzMg4/u50hNMBjlvClS54MQaq1RxFS2vV6VlCqUkpYfMjlVJjMc53ODOs+chZ0ppYk3NNG18lsqtwilxNA1vCBEa9/4ZT4DuuvNNeHjxWeenIZhSLPZJBKJrKmYbeLjhaEJdg/E2Nkf5a2zZaqtkMPTZbb0RBnOOPih7Brvd+CGqpr5R7s13EByuqKmwI8sQ82TXNsLl9OmllLiuS6mZV0wLUqgpr3HEqpKO1uHfEMNKGxLQrRe5wf/dAThqjZ9udTEtHQO3L+bF35+EoDb79tL31iMwnIDgLfenub+h67gu3//Ls2G130vO2Jw74FdvPXWNNffNEaz4RLRYTHfoJHNMF8Led2XuAHcPADDsUs7hxcakph54fjCS4EbSE6V1bS+QB2P3qjG3X0hi9MlzuTraFGHK8cTeBmdZydChmLw1qLkrhHlqXjijOSW8QTJuEW96uJ3rpyrjkHH7Ht1S3+2riQS+3Ibf2bb1AFl67S6VXkp2FwXNgEXPg/8UBKPmCzOVUilIhw7ukgQhPheiG+ERKMmnhcyP1eltyfK/FyVMJRq2FET6JqGlJJKtYUdMZmfryCFhpQhxWKDwDDRhIsmoN4IKC5UGOlP8e6S5I0FyXW9l+JGoqAJKCw3aAWSRqBSy4TsCAUkbhCwsFSjYms0vYDrMw7pqEnLg5PLjbanb2f8S0ljNCBiaJ+ZRKbNNeHjxWeenHqex9zcHMPDw9j2pqD79w1NCPYMxnhvsoIbhJxdqnNmScVDJhyTPYPxtheh5NczkmvaGemWLtidht1p1YJ/fV7yzATcPqBa85eCIPApFAvkcjkMY+NKgBtIzlZgZ7syqwllb9SxOJovhjz55BE0P+gS0zCUtJo+zz19lHvv28WzTx3lV88eYfAPryGTdWgEksJ8g+Shef74z6/nzKllCssNUqkIiaTNO29Pc9NNoxw6OIuU0AyhJ+uwPQtXZNQimW9K3lqUDMbkmtSoUEp+eErFuV7bK2j4cLSgLJWKLbi2l26V+VImeM9FiLrwfWFI4OhCRaguVPib7x4mDEJMU6fswaG4zn0P7OWRbQ5/dURdFDuE8bpeeLcqOPDgHp768WEWF2v4gZJJOI7Bfffvobcvuk539+tpyT1ja79bN5D8elayNyOIWToLCJp+SHiZWo/NdWETcOHzoFDzSEZthGlgaBpuK6Cw3MCyDMqlFqWikqL0DySI2AaHDi2oYUdNdQfCUOK6AZGIiW5ozEyViSUcKsUaqYTB0mIFp+OXq0Gj3GD/rgxbEpL3l0J+cAquzMCe7FqnkHMRSkm+JgmjEZq+xNIkQbhCNLX27z6djlD1fTw/5MziWomNICTtGAxnIqSjBuWGj2UoScxnJWhic034ePGZJ6eb+OQhbhvcuj3NXNnlxEKNji91teHyxukiN23LYBkad42cb1EW3NgvWGxInp1QmtQrcuJDVAmVZGB1spChwXIL3llcIcbddxUaXqFE4IcIP8AP2qSojWbTp17ziMYtNYE/UaZ/MIHQddJ6wKHjBWbzTT534wjJpM3iYg3d0Nh3/QiHDs5RWG6SMAW6qaGl49S9sKs7y0UEFVfin6Mb1YTg69sFs3XJbA0iugoTGImrFJefTkgCYIcdXtIE77mI6IJd6ZVhi2KhwZOPH8Z1/e77RxMRPDfs6tr+bK/GPx6XvLuktML7ewSvzoe8WHe477FrqCxWyC9WiSdsevoStCyTnxU0DiQlti6Yr4X8Yoa2DZfyml1sqM9RceGOIcFQTLAYrhyIU4t1dvTH/ln6L27i9wPVDpf098d5+vHDHHhgN7/59Vk+/8Wt6LpKGGs1A6oVl+XlOslERHVkBHhe0NWc6rqgWnWJxy1qVRfbMYk7glqxCsZKVbKT/gZwdY/GtrTkeEE5hezvgR0pgR+ELFQ9jhVCJoseTT9kseoTMySP7IwTscBtZ6TqQuBYGgKBZelsH0+z1GhweqlBKBVx7bgI7x2M0ZdcIWS588itNrGJjwqb5HQTn0gIIRhM2QwkLbxA0vRC3poogwx5Z6LEtVtSmBfIPQdljP+NHYJ8U/Ld42oB3525vFb2S3OSO4ZXXqMM8yVvLqjhqBv7AJTnqmloFAtNbA3qXkjgr6d2xbbBfLHUolRsEB9J842v7eHZp47gNQOa5QaPP3uc0f4o93xpO2+9Mc3xU8s0A0iYYFsGBx7YQxA3eH9Z5dZ3ipxpGxbqiniei8Go0qq1j2737/eMCian6/zt948gVqXTnDuEcTG0GgG//uUptoxncFs+UkoVUSoktXITO+fgusrCZnRbhtsGAt5YgOG4ZCAquLlf49ByyE8XBDcPpNi1JUMgBaEMabWUNvVnU+BLiaMpbXG+IZivS/ZkBNf1CnRt7c1KLm6RjVssV13mykrLu2dwg4OziU18CAylIyzXfSzL5sabR3nrjSluvnWMWtUl1xPDc33VNWn5RB2DyEACy9ZpNn2EEFimRhhKVW21deLJCK4XcOC+Pbz/0tsElRZ6ZhiJfk76m0LMEFzTC/t61GDoqYLP1GKZuWrAUBx6IpCMwN62jD7fbPDgg3t57ukjyFB2HUcsS+e+B/YQdQSjjkMubhFKSdw2aLgBhi4uutZuYhMfNTbJ6SY+0RBCYBkCy9C4dizBOxMVXM9nutBkvOfSsvlyEcEf7oKzFWUL9cVhJQM4H/JNycFiyO6MYGdK8O6isoZaqboJru2TPD8h+dWM4LaekGbNo1RsYNs6rZaP0AS6sX5BT6UinDi+iG0KRvocRuMCEYvyha/so5Gv0qg0EFGHWC6OnjL43BfG2bErx9xSg2jSYedYAl0XzEwXYbHBoaLD9tEEEUfnC0OCtxc3Jqcb4VQpxPJDnnvqCC03ILlK/nDuBO+F0Bm20A2NQrFBEITKoKl9HQ2DANcNsCMGxUKDLSLDWEKjx1HhBB1ckdUwdck7C5LrekPGk+2qsC3Yljzf93X+71ETgiuHkxyaqZCvtJgru+zsl5tWUJv4SBCEkqxl4Xk+0ajJNdcN02p6SAm+F5BMRbAsnUTSVhP75RZfuncXP33uGL4XUG94CCHI9US558BuDF3yp39+Pb964iWWF4oA6L6LFY1fMP1NE4JbBwSvTdRZIOALg5J0TGXYRy2NdNTEMjQ0AZau88d/fC3zc1UKhQaZjEP/QBzHUYOeANFVJvqbhvqb+H3hM09OhRCYpnnBAZhNfDLQiRiUHyKez9IFO9OqLf/KvKp4rrdiEei6QSoiuKZXcLoE03XJ1oTgqbMq4nRHaqU6t79Hg1qTidMNXvj5CZYWazzyjau7pUxdEwixUtmMRAyiMZNqxWVgKEGuX1VCJqqSd8saD+3KoIkMy03JU2dCtgUhtwy0zfa3Z5ipwexyjRd/cgSv3ZpzQ8nbEaXnzPU53Nh/acej5ksOFwS7vCoWAQ1UVdIQawlqp9J5IQ1qZ9iiXncZGEisBHp2+oKA2wqwLH1NazJqCLYm125rZ0qwJQ6Pn5Gk7bWSig+LrT1R8pUWANWWT8q5+GTx5rqwCdj4PAhCydG5GuVmQLwlcSydeNSi1fLR2r/5ZtNT1mea4PixPK2mx9BwklwuyqN/sI+JiSKlYpPe3hhDwwliMZOTR6ZJt45z5/UGC3t2Uiq6ZPpSDO3ZjRW11hFTPwipuQGFmkel6dNquIwn4arhxHldTiSSiAPj2xJsFcl2h0N2iekmzo/NNeHjxWeenFqWxejo6O97NzZxCSg3/fa8KIxegh3QRtiaFFg6PHFact+WtcNShmHQ09MDQNaAbB+MNhSZ3ZFSesZnzkquzELWhiQBs8s1Xvj5KZYWa4Sh5M3XJ3ng4Sv44fc/wLJ1UqkIpVIT2za4977dvPLSWXr7Ynzu81tZrnhEdJ13liQHxgSybaqfsuDuEXhzAZabkmxEeXtmCfjrZ45QbwbETOX5amlijZ7zUr1PHR1uHdSY/qCBQBAzJBWXdb6lnUrnhbxFQwmZXJRioUE0ZmLbBq2Wv+Y5mi6QQpDsTWxIdHVdw9DUVL1FyN0j8MKM5IY+5SV77rBWx84r34RmANNVyUgc9ves93+0Ta2rnZtcbpIavjg53VwXNgEbnwfH52ssVFwSjk15oUJVQm5PFE0TmKaOaWnkcrGuhntwKMHWbRl0XSO/VMdzfXbt7sUwBM2GR6PpU6u1yGQixD2QGqRSIWIsigxq2JEWVSyqLZ8glBRqHi0vYKnitrWhK8NNo5nIeYnpanRI6SYuHZtrwseLzzw53cSnB/OlFiDIxCymarAl0aEcl4fhmOCuEZVLfefw/5+9/wyS7DrPdNFnbZe502dVZvlq74BudMMbwpEA0UAD9CREQKR0xNHRaO7MDenql2JCERMTMVLMj7kTM3HmRJw7l7ozRyNDihApwhKOIAiA8K6BRne17/K+0ufO3Ln3XvfHKttV7WAaAHs/EUB3p9kuV65897e+7/2gLXrmbXTYgq9ugLenoNJS5vWTdZhuCNqqNSoVl4bTWix8OnmiwHU39HPn3q00mx6BH5Btj5FORRkfK3Hr7Rvp6Ezw/HPHmSk3+cb9e0iZGrWWyhldoDOmcUuP5OlhSV9csiWj0ZiooPs+ER1KTUhYEnP+Lv58o5wLaELQHoX6vMg3NYGpSRq+JLos5WF5pPNMSCnp6kqg6RpvvDbMPfdu58knDtNsKIEqNEEsZnLn3m0MODqFuYC2iCpaM4Ba1WNkuMTMdI1cPk5ff5p8wuDWbnh3FvyGR322SrXo0JGzSeSSvDSnkY1AVIPumBKlR4qSJwalsvMyVTX0XBM2pASGrhH4crHlZEjIh2Wm2gIETS+gqyPBG68MsXt3Fy3XZ/8H41x3Qx+1aoPunhTpjE2j2aJabZJIRIjFTVxDMDVZJQgC0uko0YiOFjMxrDgtVvtotoCT0zVG5m3nxGKUU6IB2bhJKqrTnrBIRsOf9JDfDi75kdxsNhkfH6e7uzu0h/iMs5Ar6HoBz41Ifn+74MPm6edswY2dcKQoubELQOB5LebmCrS1ZVdYSQkE13TApKMsqjIRwc09gpOzLSrlxlLL0Xmef+4EN35hPW+9PkIyFcE4WcAwNTRD44pdXTz/3HEKcw5+IJmdrLC+K8MbUwG39rBCGKYswdc2wKQD789BZtYBBJYGRkRSbUFNqgU5DRicdOjakOV8672klHR2JbEsHdf1iZlK9Fq6sqNaqwhjLYQQ2HGTr3ztch768X5ef3WIu/dto1ZT16d/XQbL0tEMQcGBqzp0jhUDBorQVqvx4797B2eZt6ttmzz4/avo6Y1zlenwxCOHqDV8XB+aPqRjOt/6+g5yHbEVS51X5gRX5iQvjoMnwQtguAobUtAeN5kqB4AyHD/dM/d0wnkhBNYeB73ZCIOzDZotn1g+gdvyqdVcOroSSGBmpo6uCdpyHr29aaJNnSCQmIZqyxz4klRax9B1AhlA1KDsBtRqyh2k7ELChCval77Is1V3MUKaiurYlk7aNuhKRy4ZK6dPm3BOuLiEJXgoc92Qzz65pAVIak0PPwg+cmFLb0KQtARPDEpa8+FBKc88FjptwVc2aLRF4MnBACtmkkpFVhU+TU1WefXlQW66ZT1XXt1D//oMGzZk2bEjz/53xxaN9wMJpaLD5rSgNy74xSlJcJoQtHRBf0JwbR70hE3Nk/hSiceUKUhbkLUEaUvQm1fNCi6EiK2q8i1LR0MQN1X6gmlpi0UYZ0IDWo7P8IkCb78+SjJpcd9Xd7Dnyh4mJmrYcZNtO/IcPjzNL589RjYVZWuzyPF3x+iuldhgeKuEKYDjtPjR379DpeLx0M8P0Wj6RDRlG9UeBTMIeOrxwzQdf42jEtzaLbiuQ3BTl8befsHJ8lLLUwHnPW7CeSEEVo+DjbkYnSkLQcBQpcHNd27h5ZcGCYKA/nUZslmbRNIilbIZGiwyNFjE8wNanqRUdBgfrzA9VcVt+Rxt6PzkqM8Tg5J3piX1liRmQMmVjNYkXqDmhPp8Z6f+bJSr16e5rDtBTyYaCtOLTDgnXDwu+chpyOeHQk2JGAGsiwtGa3Kxt/2HZWebIBOBh09K7uw+t7ALpKTmga1JjEScWNUlGjWoVpos15VTk1V++cxRvvbNnXREE7zw/AmaDQ9zWfWrLiCZVsvml7epdoaPnFSpBunTCoGSJmxfl+TNiE6p7pMwJZYmFouzLEuntyeJeYHFtQHQ3mHznQd2MzlRoVhwEHGb7u4k7akz+5wu+Jou79V98sQcO3d1ceToLEbEYGqizNhomaht8o1v7+KRfz7A+FiFVDpKY3MbftNTwnThVJddP6feYmSkRHdPkhMni3iBKmbzA1XUIZoex4crXLb97GkMtgGTdcm6jM5sRT0Wep2GfFQ25WNMll1qDQ8tGvCFOzbilJq4bhPbNtB1jUajRUdHnFg8wrGj0wQePPLIQXK5GIU5h1tu28jOLVkuz2oESIbKUGiq1r4xQzBShbwNhlCpTMWay0ixQSZmhD6jIb/1hJHTkM8Nc7UWEo3NnQmu6dJ4d/rjSejvjQvu6BU8M3zm7c02VARDEyoq98U+ja6MQVsuxu1f2kwuH2d5ECMWN9l7z3bS6QivvHiKcrGxwpAfIBbVsdsTi+Lqsqzg5m7B08NLkdzl2DGdb37tMlK2Tt1T0RVfykVP0rNFOc9GAJi2cgXYc20PmzZneKsomGue+Xo0HX+FMAVVsf/BgXH2XN3DNVd109Ob4o4vb+GB713Jc788xuhomUBKyqUGuVyMmZn5LjQLVW6nMTtTo7dTtd5KRyBjCdqjgraoajTQqjrnbPpkGwJfQiKqL+7qQqPLISGnEzE0Lu+OY4gAgcSVHqlOm3w+htvymZ2t43kBjWbA448cxLYtfv3r40yOV/jg/UlGR8pMTVZp+T5yfiVkQ0pwVV6wIyvYmhHc0KkKHgEu605gGRqB1Hh/tMpIofEpX4GQkE+WMHIa8rnAcX2qTY9pR+famIltKZFyqKBaVX5U2qJq+XysKmlvX/38eB1eHFOV81/ogoiuMTNVp9X0iUR0vvHtXRSLDeZm6ySTEdraY9SqTer1Fldf18eLL5xELhNFC4LyUEsn78n5CnNB3lY+pVPOUkvUBRainL///T2cGqlQLTmkMzb9vefXzelcLLgF2Absahe8OaWiuKcvgy/4mi4XpgvMzDo8/csTfH3fVlpewOhoCYDRkTKamG/bKCUTExU2blrjQi+jPRdncHAOCTgeoEtMTaAhSJqQPY9iLYBvbBRomIsV+yem62zrjJ/rbSEhZ6UjFSGftFZYC3mBpKMrSbXcZHKiQrnU5Pqb1vObF09weGBmxfvjyQg/P1RlqupxfX+Mq7oj6EIQSMm7M2DpsKtNbdvQNa7ZmOXQWIVizeXYVJ2ZisvO3kRokB/yW8klL05N06SnpwfTPLe9TMinQ6Pl8/5ohWpLMN7UF42hb+qCx05JAglxg0XT9uX4wfmbrl/fbfDo8SwbPZ3Mad+MXW2CXW2C2SZMFj0SssnPHnqfTDrKzbdu5JmnjmAaOrZtcurkHOVig5tu2cDzzx2nrc3mO/fvplxuUCo5ZLJL7UFzRcl7s6rT0wKb0vDEKckDW1d7sS5EObfNe6IuWCt93JlQ3XHBiTIcL8O2zMrnFnxN10Kils0nJio4dZWGUSwu5dgu1HsdPzrHzbduJBo1aTRWV9DbMZO+vjS/emmQjAWtABxf+bMmTIhFjPMq1gKVt3tiur6YPZC2zz3thfNCCJx7HJzueWloYNiSZMImlY7wzz/9gKGTBXVjJJaMfyNRg66uBLVyjagf8N5IlfenPSxNkEtH6bLFojBd2rZgV2+SwVmH4dk6RefCmpGEfDTCOeHicsnfcmmaRjQaRdMu+UvxmaRYb/HmYJm6K7FMg3W55REvwRXtgnemJV1rBMIkkodPnv8Srq5p3NYf4Vejaz+vAVrJ4bVnBjhxfI7ZWYfjJwq88fow99y3g91XdhO1DTZsaOOGL6zntVcGAdh9VS+6IdiwRS2b92/KYs5HOrdlBI6nRNcCnbZg7zrBz06sLpBaPDcp8YNP1qvw6rxq0Xo6gYRsm40vJc3TQpdi3ou0vT2G47RwnBaZjI2Yj5ouHK5uCA4dnOTB71+JvdwUXyhh+uD3riKZNPjyvh04UkcIlXebNKGl6dx693ZGPZ3DxXOf/8B4hdE5B0HAlo4YnalzV9qG80IIfPhx4Ps+mgZXX91LJGosjn8hBFHb5N77LiOdtrhqfYquhMa2dMCWaIMeo45XrXCqIlmj+zGTDmhRm9h8QxI/rM+5aIRzwsXlko+cep5HqVQinU5jGJf85fjM4AeSyXKTE9MOrUAQixhc25vkyWHVZm+hEGh9UnA8Lqm6ED3Nl18gSFqS8Zqk+zwKp4LAx2zVSZlRjhQF2zIr37OQZ5lKRynPRwN1DY4cnqZYcNh7zzauvaGfmakac7N1br9jM+3tMWJxk6ht4Em5Zm7lF3vV0S6nNy7QhaQVqAKJTwPbEER1VTG8vIhowYKqJXQMVi7t+wFEIhq9/Wlee2UI1/VJJCPYEYNG05uPngoMU+fAe5PouuBf/usbGR0pMTNTI5eL09WbpmoatIDNfTG+//09DI2qYq3+Dpuu7iQTvs6cI9l19swAYMGGJ0CgxtVEqUlX+uwCNZwXQuCjjYOIBZ1dce776g7KpSalUoN0OkoqHSHfESdiKafSq9alOD5Vp1hvIZCk8Ki6Vf7xAwNPCnZ3RxECfF9yYA5uzreoNT00wDhLG+aQj5dwTri4XPK3AL7vUyqV8P21bGlCPi1OztQ5MlmnFQjSMZOr1qeJmjqdMcGhuZWv7YgJptZeZVb95meULcu5CIKAer3GFzolR4qSA3NLlTq+hNGxCkXHo1RzSWfsFR6r01NVHvn5QZ549BDvvjNOV2+SsdESjz9ykL/5H28xOlwmcIMzfOHW/oH57lZxwcL0g7mPN5Lan1j72kZsnbvu20H0tAPUTZ2vfGUHsbixaFH1wYEJ7rlvO9Goga4JUmllgWNZOqmeNuqWwVwux5W3bmTbrjxeROeVcRUSCoBkQmfXjizbr+zhXS0NEY11Cbg6v1Qwcja6M1EkqkvUyRmHgYkaJ6brZ31POC+EwEcbBxJJNmvSvy5NV3dixZ/ZrLnYMjRiaFzek+ALW7Ksb48iCEiIFuutOhutGidG5zg2MsfgRIF2r8D4bBWNAEMT9GRCv82LRTgnXFxC+R/ymcMLJDMV1YUlaulEEkn2z0CAxNSg5K58/fYMPH5Kko6wyloqogtu74Vfj0hmHMme3Lnvx0xdcN8Gwa9HJQ8dA01IEpZGatpBSihXXOxEBMsycJueyvmU4LV8JserCF1wRa2T118ZXlyWf+gf3+O+r1xGJmvT3mGfV46oYKltZ9OX5zTXd33JuzOSnW0fXzRFIPHlyqjp61OqM81Yy+bb393DzKSKamayNql8koM1jaAu6covWVQ1Gx6//4NrKBWX8m6TuSSDro7vSDxfEkHSbEleGpeodNWlDmBSSnJR6InB3x+WXJ6VbMsKTE0Z85dd1VL2dAsugA25GBXHo+y0Fs9paK5BZ8oiHgmnwJBPjoVe9n3r4gghztnLfuN8/ujIXAPb0nFaPp22eu1Sxir0ZCKsa4uGxVAhv7WEM3PIZ4aRQoOT03VV5ANIoZNOJ5luCNYl1WsG5iQlF14ch4ylKtuzEcHedfCrEUmhyapCgrghuHeD4JXxgMdPBWzLCLZkWEwNWAspodqCr2xQvdqFEAx7NkfmFeKRDya4597tPP7IocU+8pomQIN79m3nld8MrsgXbTY8yqUG7+8fZ99Xd2Cew/ZJQ6URTE5UGJp0SGRsOrqSJOI6iTPk41u6yslcLuo+Ks2AeaGomG6o67I1rYo/TFujf1OW9cuKs3qk5FhJNSq4Kqdx1aYs2nwuajpnLxZyHS8FJEzJ/hnJvesF1Zbk6SHJ5W0qWvvKhOSmrpXncW2H4Oo8HCvB+7PqMSkhb0uenYbNKdiTV5/t0aLEDZSX7e7+FCXHo9LwODldQ0Py5qkyN23OYJ2jW1RIyEflQnrZb8zFFkVqpeExW3WxDI2OVIRGy0dKwjalIb/1hCM85DOBlJLjU3UkYv4/2N6VoDNlsG3Z6/oTAi+QTDvK3unVSVUYtC0juGed4PFByYaEZLAqVkUQb+rWaPqSgQL89LhkQxJMDa5oV73m35iUzM1KEk3J+gxsSgmeG5Fc2ynptLUVrT5nZuq89MJJ9u7bRr3WolJpsH5jlkbd4+23RpiarK7Sh6VSA93QmJyo0L/pzObxaxncA2BofPGeHQyYNtd2wOk7COYF9XgdeuJiURB+lKKp9UnB8SJsz6p/F5qSDhvWJbXFG4YFC6oF1iXVzcSVOZh1Vj+/8PdNKZhxIGaoCPcLYwE3dQm644Itaclrk/DQsYBWAF/qhe64EpGaEGzLnO4iINiWkXwwBz87LrENyYwDnTGYqEtu7xFkYiaZmEkgJUMzdSDg1RNFNuRs1rWdlrAcEvIZIBk1VgjRRBjpD7lEuORHuq7rpFIpdP1TqjoJAVQVa1faYqLkohbDNIo1l2TUIGat/GwMTdAdh+44gMANJO9Ow/NjEl3Ac6OQttaOHkZ0wZ4c7GyD2SZM1eEXg5Kr8rAnr1OPx6ig0fBVf3Y3gHenoeEH3LNeZ99XdvDk4wOUS00KBYdHf36QfD7O3n3baDQ8fvHEAJapI6VcZTOTTkeZmqpSLDisF9kVgm05axncA+AFPPfkAHd+Yzf/67BGpy3ZmhGsS4CmKdGWi4LV8hk+UaIw55BtW7KtGqtL1f7zPPI0nXnv1d44vDG5dC0PzcGNXecXlU2aYj6SuzaBhNcmJdd2qiK3hs9i4ZqhqYYEXiAZKEqeHII/uEyuiHarQi2otKDlQyYCe3KCPTkxv31JXQW1WR4cXd8eozofkZISBmcc+rIrW0GG80IIhOMgZIlwLFxcLnlxahgGuVzu0z6MEFSkdGun5N2hMuWGz3S5wUzFZVd/irR9ZpVjaYLrO5VR+6mK5J0paIuefV+GJui0odOG9Uk4XIQPmgJPJokbgmlH0hMXXNGukY0K4r7HsaNFDN/jm/dfwakTc4wMl0mno9gxk2eeOsoNN63DNHR0XUPK+RyxeQEaiRokkhFqVZfMWczjTze4D5D4AYvdp2TLp1WocnlbmkJDsn9acrIMXqDOeZfp8NN/HCAiA/z5iGUiqoqTImmbZ4YDvtir2iOeGclb09AVk2xJCzQhOTQnuaxN5Xh+XN0/356G9igcL0oOzMLmNXxqDU2wPaPSOZ44JbmsDaYdOFFW1yWiQ9yEtCUYr0sSRkCx7pLWWgyWAzRNJ5O02ZLVuTqvtum4PrqmZK4gIB7RV/UoD+eFEAjHQcgS4Vi4uFzy4jQIAlqtFqZphv5lnwE0IbhyXYqZqsuhsRoRQ9Dy5GIxwdmwDcFlWcHWdMALYzDXkLRFz62kUpbgug4lJH3fQ9cNhNCo1XyKhTpOQfLwk4cREuo+XLEzx/r+DCPDRY4MuJTLTQBee2WIu+7Zxiu/OYXjtFhwjopEDfbu287Lb48jDe2s5vELBveFplz0BTV15oWUYnbOwW7LUm352KYSa3f3g+EF/P3fD9B0fTSDxaii5Xo8+fgA33lgNzd3abwwJrm9Z7XB/xKCm7sl/3hUcrgguXud4MlByXQDrmiD4yVJh/3hFGrTl0w6SkzPNiR7coLhiirmurxt7feYmuqcFUjJlANpCx7cuvq76vmSd4YrWLQYmA0oNCWB1Ogwm7RrMSDGqZk6w7P1xeSRbMxg2xomueG8EALhOAhZIhwLF5dLXpy2Wi1GR0fp7e0lEgltOT4LaEJQawZsbktQnHEYPTxLo31pefpcle6GpnFDl+TZYUlUl/QmBFvTakl/OY4nOVEGU5NYmqDaaOFXZ9ncm8MptvjF4wPcdON6nnx8gEbTAwSZTJQDB2e47to+ioXGiu5GU5NVXn15kC/duRkhBIODRVLpKImkxaEDk2iuy5f27eCNksYmKemKrRZ4Cwb32TWqzhfoydv0t8MVbRoVVzJYkbw7K+irl2He5sTxQQ9UD/qqB3G8xVzXGzoDfjkiuXc9qyKGCwgED2yFd2ckTw5K9uThvRlVjNb1IYWpuuZQaKhI8O29gqguGKxI2uzl8nv+GIRY7O51e2/AKxOSvjj0JVbvv+76HBwt03Q9LE1y3xYbQxdMlJo4bsDQTJ1qw2Ou1kIQEDU1+rM2PZnIqvQLCOeFEEU4DkIWCMfCxeWSF6chnz1qrg+O5JHHP0AEEl1XXZQMQ+drX9tBb3fsnAI1bgi+vlFwuKhsnn56XGLrElNX3pn5qGBDUi0LG0IV6Zg61AMoV+GJRwawLJ1qtUmz6c3buEjKpQbprM3AkRl+9/eu5B/+9t0VArVSaZJIROnrS9Den6E4XaUwXePqa3sXxbXlSF6flPQnVltbLRjcLxRenY5l6Ssir0lLsKtdtZ958zUHx1NFXl4Amq7afgZSmeMPTjoU0lm2pGBLWvDuDItL3WsjuDIn6LQlTw1JUhY0WnDdhjVeKc6vACsTESy3ZpRIik1J2zIxvuBUMDFWYXqqSjIVobMryZVpi6N1QV9i5TarTY/3h8v4vk/EgMu6k2RiKg2kvy3K24Nlqm7AXNVFoBoKXLshvaKxQEhISEjIZ4dQnIZ8Zmh6ASNzDWK6yeOPDlBvSoSuoftgG6BLn0cfG+Cub+wmltDpPI8I3vb5Lk+XZdWfXiDRNRiuwNESVFuSpg91T5LWJTFfZ2aqSsXxWZ+JUio6KxwJ/UDieT6vvjbKbTf38cf/5kZGRkrMznc36u1Lk0waNKUkaWuk1qfZsCG9KNoCoMMW3LMOXp6AX44EXJkTtC9LP4jYOvfct2NVUZRlqcfXih4LoCtvk41Aw1di2wtUWoBtzOekttnkYoIXx6HTVrmqYzXJnX3ijEv8gVTpBd/dCo+dhNt7FvamWG55dXoB1vl4uXoBbEwJts5/TgtOBY89fJCZ6dqiHZdlGdx93w46cnEEUVp+gBCCqXKTE9M1ZOCTiOjs6I6vqGjWhGBnT4LDEzUarQDL0NjaGQ+FaUhISMhnmFCchnxq+IFkaM6h7HgIISjUW1i6jum4FOs+Gdsgaqz0I9WDAL1S5V0njRcEXJUXdMWUCKl7kokajNWVyNmSYVVupKGppeUFu6PlQst1BfWaweBxh7QFNafFxvWZpXzP+b9IX2IIn+efPUHkXpMt23Ls3NOJHwR4XsAyW9BVNkpLxyG4rUf1yn5hTFXR72qDtqggANo7lgzsFwzuzyb6FiKuEctAuD62DjVPzj8HKVunqy3KB++Msjlvk8+maIsZzDmSX474XJlTxWQZC4IAjpfV0rsnlX/qrKOu3bSjXBIkkmNFgVmt85tnBvDcpaNaENHn02zA1MQKS6im4/OLxw4tCVOphHaj6fHUEwPsvW8HjUAyXKoturkKAjK2wc7exJqm5Lalc+W61DmOJCQkJCTks0IoTmHNnLOQT5aS0+LgWJWmpySGRAAaLd2iUSqTi+tntDwqFx3uvTbLZC3gZEWZtfuBJGnBhqQSO34Ax4pq+XxHVrAppQSsBJ4bUd2kdrerYqgFNE3g+wFtbTa6EAROi1QyQjRqLBrty/kIaLHoYJk6btPj//h/v8iD37+K7t7VhTXnotMWfHMTjNdYUcEfAKatrzK4P5vYOz3iGjeg6QsipuALX1jPE08fo1JscFSoHd119zbaBWzNxTnaMDCE5L0ZWJeC+zYKLA0WxPtQWfLOjKQ7DvVWwD8dhy93+jzz1IAKfy7Ddf3FAqwzNRtoBZJaC1LWUt6rEILx8QqVqktrmaLXhCosq9dbVEoNhK5hm4JGy0fXoD8bZX27/bF/j8N5IQTCcRCyRDgWLh6XvDiNRCJs3Ljx0z6MS4qS0+KdoQpKlGr0ttnYpo5t6WTjFsPS58SBM1dDLlgx5WxBzobrOpRP5ukdn7rjgqYvOTgHj52S3NqjukndtwFGa/DimGRDCq5va1DDxjBM2nM5UnF/MefzgwOqE9STTxym2fAIfImmCSxTZ+++7bz68iCO0+JHf/8Of/yvbySS+DBfKTHv2bqahchr05fzBV1n7v60VsQ1nbZxWz6vvDVOvdzACHymZx2klPz0J+9x1z3b+OlP3ufB719FPR0nZkh251i1j3Up+OUoTDqCd6clcUtQn60SeD4agmD+9mLhM3Bd/4zNBkpNyXOjkmxEMNeQaELZQqWiGvEpB8+Ti60aF85fokRqpdQgnY5w7ZYM05UmuYT5ibRwDOeFEAjHQcgS4Vi4uFzy4jTk4iKl5P2RKiCwTIOdvckV/c0vtCBogTO1Io3ogqvysDUDL41L4ibEDEk+Kvhin2CiUGPmNw/R3H0/NS1OT3xlBLIw53Dwg0n23beDaqVJqxVgmBqmqfPqy4OLnaAcp8XIcIltu/L4/ur4ZtNXzgBVV3Vx6kmo1qvxs/iN+lJyqKBM8HVNSflAwi3damndNlS0daah7JVmG5CLquPv35Rlg9bGyKkizz17DAATuShMARoNj3qthWVq/HheXG/IrT0lSNT+3pqS/M4WQcLSePcNR3Wl8qRKHbBWvudMzQYMHW7tFuRsQSBhtuby3GALpCSbttB1sdh7XIj5pXuhPuG2rE0qYWHqgu50WDEbEhIS8tvIJS9OXddlcnKSzs5OLMs69xtCPhJ118cLJAEaV61PL0a9vEDy/qykFcCNnecuCCq1JK4PR4qqqCmQEDcFXqCWjDtt6IwJOuftmhKm4O51ysZosg7llupO9O34KYov/0+ym65jyNjOwHgVLZLAtqLc9vUrqMxUqRXqBIGkrz/Ny78ZZGSouOhtupzZmRqG1rHg5sR4XfLGlFRFQx7sbBdsSivBNVqFVyckFVdyy7xQO51SE4Yrkjv7oD8paAWgC3h9UpnQOx7UPWWtVGtJ2iLw1pQq7hJAd0LDHK1RbUlMXYDrr45ilhrEEhYT45WzimuBwPEkD25T9k9+IAliNvWWEpsxXb1qOWdqNhA3BLYuOTxR5d3xJoNln96EEr/ZXIJYzKRaabK4xfnNRqIGqXSE9lzsI7VkPR/CeSEEwnEQskQ4Fi4ul7w4lVLSarU+8R+7EJiruQzNNQClN1wvWBSnDV+Jy6GK5H8dDtiWjXLHN3dTmFLL0z15m+7uJJat89RwgAZYhsolzdsqqtb0lXgzNcFYVfL2tGRjCnbMV+oLBG1RFo35dydrTP3khwTZTTTrHu2igjdTI98BXbkUEdugFU/x7OEJSrUIQsDBA5NnPL/2XBwvUA0DZhtwtCi5Z52Y7xQlsZb5rLZF1BE53toFU6COc9/6pfdY86vXN3UtXMHlqH8Pzhc1OT4cLUlSWRtfqi+6560Wnel0lCOHXWC1uD6dmzrhnSlJ1ID9M5Jb2hNopo7n+Uh95RGdKcINygHgiYMF3p/y6LADbuzS6EyapG2TbFznq1+/jMcePrSiWj8SNdh37w7yHXFseymy+kkRzgshEI6DkCXCsXBxueTFacjFY2iuQbHuIdHQNEHEXCqWSZiqkGlbRlXdq6igxobNGaqtLC9PBByZkUw1AvJRuLN/tWm7uSz1cF1K0JeUvDUFj54KSJmCPTnls7mAUThFtFWmfv3/i3986DAiWcHFwDAMIpbBfV+7nErZYWKsClS58ZpeolFzha/pArZt0tefplFtMT5e4dSkw4ZOG9FcqLBfe/n+zF2azs5wVbJ/RkWMO2MC21B+rYYGv5lYytlcn0tgWToiCDCMlbmZ0ahBLG4uRoEXxPUSqt99dF5UHyxANgJtNtzULRBC576v7ODpJwYIAp8F7X0myyspJZPlJgNTDd4Y9/hSr2RLPkZ/m71otg+Q6DB48Pt7GB+rMj1dJZlUPqeJpEnE4hMXpiEhISEhny6hOA25aGzM2bwzVJk3QtcwNMErE6rDkUAtUVsaRA1ImMqbNKpDVJd8dQPMOoK4qZZ/z1QUtBxNCK7rVK8dqUpem5TUPUlEh2vTdXjmh0Su+gOeeOIwbqOFLqfR0t2AKuh59eVTdHUvWRAdOjjJg9+/kh/93Urjfds2efB7V6Fp8E8/fo+y49EK4KQpLshW6UJoi8Jd/YKKCyVX2S1JCVOOOr87egXNAKK6wXe/eZnqcsVSG9jofEvVV18eXDyHvv70iiX9wTLsn5Xc1a8+k6oH39qsBO6hguSNacm3Nsf4we/tOaflVSAl7wyWqDc9dBlwZx+kogYbcrFV5yaRRG3YtCXJlm1p9f4gQMpQloaEhIRcCoTiNOSikbZNrl6X4u2hMq4XMF1pclNXhJu6TheaktcnVQGTPS1pjwpSJqQi0AzAc6DhS0wNNqbOXAy1nL6EWGx7WfMkyZlTlKrDzDpRfNdD1wDfRZM+jmdgG5JyucmWrRYLYcgD76kl/T/6f1zP2GiZ2Zk6ubwy3o/FDf7+b97BaXo0fEiqBkXnZat0vjie5FQFtmeXCqmGmpJKa74zExBIdbA/Pyn5+kb1moUK/qGxCuWZOjIIiERNVdA1VV0U1/GEgTe/r2JT8uK45NoOwcsTkj3t6jMYqynbrqNFuG+9upkQMYMNW9oA5V27luXVkYkq9aanPEnjxnxBU/Ss5yulxD9TjkFISEhIyG8tl7w4NQyDzs5ODOOSvxQXhZRtELM06q6kVG+RT65VcS24Mi+5tlOgAcPV+a5HAcy1VJTQ9ZVQfX1SEjflUstOAb1xwY6s+nuxqXJSl5OlTvnFH6LHshSLLsKwELEcrUDDdXx0y6cldApll3QmqpwDmkokHXhvkgPvTZLvT9PRkWTTtnY0QzB0ooDT9Ki2lDDVl/nhnc1W6UKIGOrcHz0p6Yqp/ajzXNqXF6j2nAMF+NvDkhs6IWmBqWloHRmSHVnW6x5Dg0V27urkttwmevvSJJJLwhSU2L2zT9AdFwgBT49IOqPKOSATgStzgrQlzrs7VMlRwtTUBVf2f/YN8cN5IQTCcRCyRDgWLi6X/FXWdZ14/MLN00M+PCq2J2iuUaCzgLUsB7E/ccaX4Qcw04QXRiURQ3U3OlKQDFZVDiaoiKM+3/c9HYEvaiepn3oHM5Unm0sSxCTlUmNROApDNaZPpqKUmwF33LOD536x0jlgeqLCtit6eX5C8qU+mJl1qLYgYSlhupDzubAOfSZbpQtBQ7CrDS7PKvuoiTo8OyzZllHWUhFdMFaDd2ckCGUv9cGcRBPKtqrclJRbMB4VfHFXHkPrwAskvh+sEKawVDQGqgXsaFXdAMSB23u1xTajZ3JUWJ7GIKUkCJQ/a8b+fEw54bwQAuE4CFkiHAsXl8/HL8UniOd5VCoVkslkeEd0kVifszk4VqPkeEgpP1LXjZkG/GpU8tWNgrixYFG/tL1ASt6ZhpmGUoXrrTqzz/yQShPE9DRd/d00G3MrIprS9xBCo+V69ORsjjUNbv7aFbiFKk7ZIZu1sbIJihhQDXjkBGxM2KQsgQwkTdej5QWYhoZp6WiaOKOt0odBE4JqSzJZl2zNCKoe/HpU0gqUH+ot3WKFuBypSfZPS77crwz0j5dhvOqvau16NhoeVKWK1h4qBGyJylXCFNZOY6i5Pn4gAUlf29mX8j8rhPNCCITjIGSJcCxcXD7+1iqfM3zfp1AohLltF5FUVH2xPT/4SAUuxabkiUHJl/vEfA6mWCFMC03JM8PKeP/udRp3r9O4nFOI0XeI6mCmchQmpth773YiUQtlcw/IgEhEY+892yiVHHa1C9pSOl4uw2xXN8N2mhemNaJ6QADETcm29Uk0AdNTNQoFh2qlSaHgMD1VQ2jQdQZbpQ/LppTgmrxgoCjpsOGOPsF9GzRu7hI8Myz5zbjkcFEyNF9strNN8OSQauXaYcMTpySuryr+j5fOflylpsohvasfbukRDFYEg6MVag0Px1eiczkLaQwLNx3NlipmEkCj9XGWhX1yhPNCCITjIGSJcCxcXEL5H3LRsQyNBSOoli+JnMNOyQskc02wdVWpP1BUS/UnSvCl3tUG9mVX8sqExNDgpi5BylLPW16N8q9/iKkJDCGxNuziWDXKsePj7L3vcmq1FuVSg3TSIB7XOfTBOBs35+juS5EwBTsysCEpmW3ASBXemIJvbhIcLgJScsNN6/nFYwM0m0uL5JGIzg03rgfUneCZpFnTlzR9Fo/1fGiLCm7qhLenJXUPTE3SlxB8Z7Og0FRV/A0fGq7KvfUC+NkJyZ52KLvwT8dVZX8rQDUHWLOwTHJgTll8Ldhe3dIDb7/uLBZi+XJR1i8yPu3g5bLUW5L+uIZpaLQ8ODReI2bpJKPh1BMSEhISsjbhL0TIRcfzlyyB1oomHpgNODgHtgk9MfigoHJJLUN1WtqSEfTFBVfnWOGPucAHc3Bth6A9KjhclDw9HNBhC27KBjR3fQt2fpOoHacZ3YQYdjg1OMKpwYMkUxHiCYsJXVCvVDGsKJlrVy7HWxqMVFVRkClgrAa7cjpHDszx8kun2LtvG/Vai1KpQTodxY6ZvPzSKXRd0NefJpY01xSoozWYceD6zvO/jhVXUnYhGxHELdUNa6oueWYk4I6+tbpOCcqu5O0piaWr3NUtGcGvRiVBAGu1qJ+oK2eErZmlbSVNQW+HzdvMF3+t8b5cu42lSVo6HCzqXLsxy2vH55CB4P2RCjduzqwo5AoJCQkJCVkgFKchnwpCKF/OwxNVdvWmVojMbFSQsJTwKrvwu1sFxhoi9Ezc2KW6RQ0UJMNVybc2qUiiZiVIXXYbAkHL8fn5j9/j+hvXEbEtnIZHsdyiXHHRNEEiHSOyRpejJ4ck+aja/ogDc01JX0owM11jaqrKoz8/SCoVIRa3ODLgLhrcz87Uabk+W7bn1rSUmqorwXg+/q0LRA1o1gVbIh5TYyUmD9fI5eNs707z2qRBsRkQSBVxbgZKWEtUFBWg0lr691oiH2C2ocSv60NEV9fhlfGA3myC9oROsMYyvWXp9PUkMe3581FWpezsTfHecAnXD6g1/TB6GhISEhKyJpf8r4OmacTjcTTtkk+/vWhYhsa2zjiHJ2qU6y2myk26M0uFMr1xQW/8w0XVik3J4SKM1yTrkoI7egWaELQvq8MRQjA5UcF1fT44MME9927nyScO02h4SCnwAzANwc13XcYbJQ1RUub94zW1BB7RYa6hquG/3Cd4byagPb9UxVkuNxdF6QLtuRjDQ0WS6cgKS6lAqvzQpCm4Mn9h5xzVBJl6jf/1o/3UnRYLGtq2Te5/cA+DsThjlYA5T7kagBKjmoCoriLM5Zake7UP/iL1FsRMyT8cUTcUu9qUqH3b0bn97h289PQALXdJoJ6pOxRAJmZi6BqBH/D2YJkvbMkstq/9rBHOCyEQjoOQJcKxcHG55MWpaZp0dl7AWmrIx0K9uZRUblsfzZx+ObqmbJWu7RArvEaXowkozDmA+vPggQnuvnc7tapLqdgglY7Q25tCtySbrYCBukaxqYSppcF4Te3DEIJURLDLCmiaaeyoieOs0do0ZtLdm+KZX53EzsaZSWW5sh1A8u6MyqM9mzAtNSWTjrLUWt7utF7zeOhH+2k0WmhiwYQfHKfFQz/ezw/+5fUIqZE0BTlbieqyqzpuxU21nTemlIn/mRBCcqSg/r49I7A0yR19gv81IBmK2nzjd/YwN3X27lDL6UhajBUDNFT0NBP7bE704bwQAuE4CFkiHAsXl0tenC50odF1/SNZGoWcP4GUzFRdQBCLGGRi5se27aQpFrsznXn/kG2zAWh5ASdOFJiYqBKPmfiBpFZ1uf7Gdbzz1ihmxOD2ey+jbNhsikiSFozVIRNB5SWgIrPJpMGDv3cVP/rbd1YIVDtm8sDvXsn+9yfQNejJ27SlJQfmJLOOstG6pfvs404TMOXAwTlVmR8A6YiGPVmiNr+v01N3G06LiZES6e52pJS8OilpeOrcowa4viQXhZt7BI+flDxyMuBrG1cLxe6YhqHBVXm1DyE0NAF/uFPlr+6fgYaVJtaXoSFgpCwJihIfFWXNRlbaes1WXTQkQkA88vHdlHzchPNCCITjIGSJcCxcXC55ceq6LqOjo/T29hKJrNWtKOTjZmjWwWkFSDQ60xZvTEmSJmQj4Em1ZL4to0zl3UBycBYcH7pj0JNYadD/YZBS0tmVxDQ1ZmfrJOIWMpCMjpaREqJRAztmUiw1EELwm6cHuP/BPVi2zquTAe1RODAL13Yu68wE6Lk4f/Svb2R0uMjsTJ32XIzunhQHD01w9IMp4lGdSFuSx08GbM8Iru8UKyKhZyJpCW7phro3vywP2Jbg9YM1BEpwLpj+L0RQQeW5JrpyHJ4L6IgJ8hnlVyoRGLqg4kp+djzA1CBjwi9HAm7tEVia6szVdHyakxWasw4jFZtcR4J63SXwJKVSg2rVZV27TUdXEi2q43hqzwsfT/Q07RlIcL0AgSSfsD6zS/oQzgshinAchCwQjoWLyyUvTkMuPiXHY8F8qCsTxatAuQWnKmrpPKpJfn4CNCGptmBzWpCLwnQDfjMu2dkuyUaUX+f5iLu1iMYM7ty7lZ/95H2C+Q5GC8J0777tvPry4HyoEOo1l6nJKv0bMxSaMFGDvA19y5qFeIHkqVOS7VmdLb0pyo7HycECv3nxJCDA1PjCXTvQojrf2SLPmHJwNmLLzlUDcrk4QrBoy7UgVDWh8kPz+RgJGzZvgKiuBGel4jE2UmJ2pkZHPs7u3jR6zOCJUwGXt6mOU/esExSmVfenqapH0hQc8AO8ls/Xv7WL5587xuhoGU0IUukosZi5qivUWhiawDQ0PE8SfIyeryEhISEhv12E4jTkopOIGBTqHgLBick627pW9iedbYCP5IZOFVnzAsk7M6r6PmFCtaVyJ9+flXxlw4eMogqoVJrc9/XLaTgtZmfqmKaOHTN59eVBpqaqixXsvicpzjms25RhoqbePu2sXLKedCBlweGi5NqtFldcnmeyLUp3Z4JYysbIJujKGlja/M7XIEAyUoXDBcmudkF3bO3XOZ6k2Azo6U0TsQyVRiBUoddCMNK2Tbp605ysBxx3BZYOvc0a//Tj/TSby9IOoibffmAPX+6Pk7JguiGpVFWXp2Jd+bVqSAqlBvG4xU9+vJ+77t7K6GiZQKoIqtAEjz12iG99dw8NXSNhqqj3WmRiJtPlgLobGlmHhISEhKzNZ3ddLeS3lo15m+50BEEwn3u6kvaoWvIGkEj++YTqD//97YJvbBLUW8pof9pRXaAulLIreWNS0tAMnn9pCE0XtLXHeOO1IR57+CBTk9UVr9cNQaYtyrFiQNxQhUndy6KmEsnL46pvfcOD2abEtHVy6zIkt/Vw1EqTT+vzwnQlAZKjJfX+Hx+RDFXgxq61hWmhKXl7WvLurEAUavzquWN8+3euwLJ0fF/ieQFSQiRq8q3v7mHYN0iYKr90a8RbJUwBnEaLf/zxfixXCdG8LZgYr1Bveji+KgBrtXyEAN8PcJwW9VqLWCJCIJVnbavlU2sEjI5VmKgLnh+VPD+6FB11PMkHcxI/CJituggC0vbHl2ccEhISEvLbRRg5DbnoaELQnjAZLzXnLZUkb0/DRF11OtIFpExVKT7jwI42we72JbG2d53A8SQjVSV6LA2uzqu81NEq1Dxl1j9el0R1yNli0e9z0lGdmK7rELTFErzVaBLMt+BsNk+L5gmBRGDFLNx4gpfHJC0JngObUksv+8Wgsq0CycE5+PkJSXtU7WdDSnBbz9rpB+N1JZLXJwXbMnBT98r2q8s5XpKcKEuQcGPG5//+2/04Tguv5fN7P7iG6akac7Mqz3Xd+izRpMH4tOSDWclVnTozI4VVwhRU5kLTaTE2UmLL5XlsXTJRdGgFgqiu8lc9TyI0ge+r/ReLDZIJi3pV2WVJX2JHwa047NmeZWebxmBF8tigREPdWGzNCKQEP5DzObPhfXFISEhIyNpc8uLUsiw2btz4aR/GJcfynEM/gA1J1S8+FYHhCrwzLbkyJ7i1B8w1CqBsQ7A1I9iagRdGA/75BMRN6IoJ0hbE46pgSUqVAiAAQ4N1SUEuKpidcnju5UFuvnUjb74+zI03r2fffdv5xeOHcV0PXdfQNEGuI859X72MbE7nlYIko0PRhcuy6pimHYnjwQ2dgoECLJQjzTaURdRgRTJcgT056Imr3NqRCkw5qr1qfwJ646zRzWklm9Pq2F+bEkyNFWg2WugaHB6Y4dChGXp6k7S3xzh2fBaha9Ty7UR1+NZmgWkIXn53Ph9BqoIw5TMACIEmYHa2znZNOQHk22xky0MEEs3QELogCCTmvP1UJhPl6LGZxc5Z+rzwzmSXuml1RAJubPPQBYyXG/h1jdenW4vS+0KaKnwahPNCCITjIGSJcCxcXC55cRpaQnw6xOe9TYNA8ubJAhvzcTpSqgKyPyHJ22JFAZAbSF4ZV6bxG5JKUE3VVZ97T8LXN5658j1vr/x30/F59LFDFGs+labHtTeso9X0yXUk+N/+8FpmpqtUKk3y+QRdPcq383BRKnP++dXo9Hy+6UBR9Z0H2JEVvDklsQ0V+XU8mGsqATpQkLw2qYqVUiYkLRVBtnRoO+341qLhwy+HJbf16Qwcqy122Aqk8nadGKswNlpB16A4V+cLV+Rpeao8yQ+gPRcHqZbmJSyW9Ash0XWN9lwMJESrNaotH7/l02yqpf5EIkIQqNfZMRM7ZlIrN9GEOgfT1LHMpW5aXiB542RhyUFgqVktGspCKpewzn3SnyLhvBAC4TgIWSIcCxeXS16ctlotpqenyefzmGaYB3exiEcMtnXGODZVp+UFHB6vIIGYpRM1NWKGBkiOFiVDVdVi8/pOQTYimKyrJfwdWcFQVTJYPv+qfSHg1EiFUt0nYcLcrMMTz54gnbJoS5hAwC23buLyPZ14vkRKSYCK5PoSYiZcn17aV3tEFWgBtAKJQEVqv9QrOFyQlFyYrEPMUMK0LQJuoNqfBlKyKS3QzqNl6bPDkmvyKircnouvEKawssSqvT222BEKIAgCevvSmKaO5y17Yl7gmpZOd3eK2ak6Tz8+QDIVWeya1Wx61OstEokIUkruf2APz/3yGMwL01Q6SiRirOgKdXi8Mu8eECy6CHSlIySjOvnkZ9tCaoFwXgiBcByELBGOhYvLJS9OgyCg0WgQBGczwQn5JOjJRGlPWLwzVKbRCjgyXgGU6NnZlyIRNZioq85G3968JGgS6aVtDFaVJ+r54HiSQwUNr+CQNEEgsPX53vN1l+OlJpr06TxZZNiIc3DWpzcObVEVAbQNVaV//bImIa4PKhVT8neHJV/qhXdnlGfrTd0aN3RJnhxUkcO6D50xwZ4cgMqbfW0SXh0PiBrQlxD0JtR7l+eeniqraGx3XCih2Z/Gipi03JU5pLqmqvR7+9Pz41kyUYcpR9DWdLn/wT089KP9OI3590mwbYP7v7ub2dka5XKTqYkqrabHwQ8m2XffDqqVJnPFBrlcjE2b2qm7Le7et41ysUG15tLepnxOl3eFUkb7SphetS5FLKJ/5pfxTyecF0IgHAchS4Rj4eJyyYvTkE+XiKFx9boUbw+WaXjBvFenxnvDJTQhSMctsskzN3+vupIrlnVYGquppf5jJVUolbQEG5Iq0nqsKLm+S9LM2xw6LVoZ0QSGJWm1oL3dpiaVbdVgBU6W4OZueGcW1iUEI1XYkVXvm3QkO7KC34zDjgy8OgG72peWgDQh2LcB3poCU5McL0nG67AxBVvS8MVeFVv0A8lgBQ7OweVtKsK6QCay8DpFWTf4nd/dw89+vExoClWl/50H9uBaBkdmA6Yc5Y26ISWYOVVibrbO7//gGqamqszN1mlri9Gej/PySydpa4sTjZkIJJVSA0MXvPTCSeIJC980mZyqUGhK1l3Rw+YUdPQkEUiC+RzWhel6puouXtnd/UlSdjjFhISEhIRcGOEvR8injmVoXL8pTcuXTJSanJxxlMCRUKo2aYsZwNqJmTd0CuLLlvRbASRN+OoGgaEJxmqSUlPlpcYMeHc64PJ0gqbQaLr+fEtO9V4/AMvQyeYT9CclvXHBIyclKQu64tCYVFHNoaoSpABjNbitB96blegC2m3Y1a4h5guNAmUEwLUdkt+MCxqBpNZSaQqPnZJsTMHudoGuCTalYVN69TlmlvmpeoHkpTHJumScP/xX1zM6XGJ2tk57e4y2rjSOZXCiJDlVhitzsD4llA1Um83bb45y6mSBfEeceMLizTeHGR9T0eq77m5TxU9CpRu0Wj6mqVOYq9NsSSKmoCdnc7QgiQhJd3ztSOhEsYEgQBeQ/Rjb0oaEhISEXDqE4jTkM4EmBBFDsL7dpj1hognBGydLgOTEVI1U1CC1hjdm0lopktYnl/VyRzJWUxX1rQBu6BJ02gINjfu/cRlPPj6Au8wM3rR0brtrC8cdKJQkjfmn7l0veOiY5LYeQVsUXhpXj7cCZZM0UYdyAwwdvrtVo+X4TE5UKMw5ZNtsOueXvW/uhpEavD4hmahJbu0RnCzDa5OSa/LnV8H+6qTqWf/uTMA1eYMtl+fZrqme9serAddkoDsmOFqSrE/Ne8XOt2u1LB3X9ZmerjF4qki9rjxmI1ED07YwNIhEDJpND68V4DgeruurcibbQHo+l+l1PHnmSPZczUVDhB2gQkJCQkI+NJe8ODUMg1wuh2Fc8pfiM0Mioj6L7V1xBiZqaEjeHylz/absBRXTCKDDht051S9+gQBo77D5zgO7mZyoUCw4ZLI2HV0J0D16DB1N03huJGBdYr7YSkg2pJaLLsmxEvQl4IUxye42mGkIxsbr/PrJwytEr2Xpi+09++KCVL+qvHd8uKYDjhbhqSHJF7pXdp1ai00p6IkLDhXUcQRBQBDA61MBd/QtvVc1MVj6d8RWx/Dk4wO4LR9jPtociRrsvWc7774/TlQXqhDq8cMgBL4fEEiIxQzu2bedgwfGGSu4/OD39gD6qmNz59MyQPJ5L2wN54UQCMdByBLhWLi4XPJXWdd1UqnUuV8YctFwXJ+pikvd9RftiPgQgTiBYF1y7ecCwLR1+jdlWS+yi7mTC1+JF8YC6h58cV7wLWitYNlxHC1KshFoj0JXXJARPg//dICYWJkw77qqHeh3HtiNaeukLEHSksQNteWtGehNsKLC/kz0xAXTjiSyTBsWmhJbZ0V6w7rESnV4uiCfm3OUdVTM5M13J3ArDg6Cgwcn2feVHQAMDpVoy0ZJJiN8cGCCwlwDI5AcH6pw+fbMqmNbLkgXDPf1z1kh1ALhvBAC4TgIWSIcCxeXS16c+r5PrVYjHo+j66ujQSGfHNMVl9mqS6Xh0fQk2ZhBwwuoLKynzxsRSQQ92ehi1LTUlIs+ox8VKZVFlBACXQjcVpM3ZnSOlwTf3qRsnsZqEnM+YDtQlGxKwYmyMtq/vE0w05CM1gWpYhV8f/5btfL4XFct9fdvyiKl5PpOwfNjki/3qaKl2HlaYQHMNOC6zqXXT9ZVy9dzsSDIM/1ptI4sVB0ee/gglvQX3QFqVRc7ZvLee+PMVn1mZyrUq+7i+ZiawCk7vDmd4aqcRF+mSA1NkLJNyk4LCPjNsQJX9qc+l0VR4bwQAuE4CFkiHAsXl8/fr8bHjOd5zMzMEIlEwgF3ERkrNjgyWYelhWCmqx7KlEnDMjQ601EMXRC3dLJxZdr++qTqyHR778dzHBrKlH9irML0VJVEMsL2riSbekwyEY0AeHlCckefoBVIBgpwZ5/g5ydUCLU3oSr0N6ZgbMg5q8gsFhzWiyy+hJQluK1btT69ex0kzPMXpwvdqRY4XpKLEd4FAqnap57u/xogGS5LjpZ8ruuI8oPf27MitaGzK0mhUOfwqTKgormmDklTLgrYzpyNsOH1Sbipa2nbYt4CbHCmzmjBASkZnnPY2XuG8PVnmHBeCIFwHIQsEY6Fi8slL05DLj7FeotjU0qYarpOf5tNxNCYrjSZq7c4VInwR1fF1+zIUXIle3IfT9RUA2anVPRwerpG4AdICdGowb1fvYxYkCCVjVBxVZep1yZhfUL1nE/NNzjaP6M8SJseZM/R6ml5e09QVfhf6FK+qLd0f/jz8OXKJX2A92ckA0XIRFThlS+VR+vhgqQzJti3nvlo8OrUhrSMoRs6WuBjaGBpgpIriRmSWFR1gjJtyaQDPz0e0B6FG7sEFVdZYPXnYlQaHlXHxQvCwqiQkJCQkAsjFKchF53JcpNAqv5B165P46GRMAUdKYtfjUhuzYkztoq7Oi/4zbgkosvFYqWOGBe0LL5A0/H5xWOHmJ6q4nlKmALU6y0ef+QgX/n65WAadMYEUioz/L3rBNUWmAIMXXWsKruSWkuyYVlF/OlY1lJ7z+V0xwUvT6j81g9zDgDXdKx+3568xp68OuZnhiVJE7piglt7xKoo7UJqw+Kx2jr3fXUHz/9igJbj4rkS2xBELItb9+7g/arOnqjk6rxgZxscnJP80zFlueX6qp2r4/qAJBkNp5iQkJCQkAsj/OUI+cQpOx4T5SapqEF7wsRxVfeimGUQNXUeOhawOQ2zDUnKEquWrZfTHhV8baPghbGA4SrYJvxqVNKXkGQigk4bIoaq0heceTtCCCYnq9Sq7gphuoDjeBTmHMyoyd71Kd6YkmxMCdoiglcmJFekAkrTVRozDTZnbNJaEmN5Rfwa1frLuygt5/pOwTvTyuj/w9B7Bs9RgA0pZcJ/JgIpKTZhrqHcCAxNoAuIWBq33rqBWrlBqdQgnY6SSkdpi+nYtmqnelc/RHTBVXnBVXm1PTeQPHa8RT5Qlfs9mcgZ9x0SEhISErIWl7w41TSNaDSKpn32+31/HpmqNDk4VgNgjObi4xJt0bf0m5sEPzshiepwV//5RQ+lFOxdB6CWxmcbUGiq/yYKyuh+Zztsz6y9PU1AYc6htYYwXaBYbJBMRYgaGgMFj9/bLtCAXt/hl48dhpbyAfW8gHjM5Kvf3El3V2yVRVXnae09T6c/Idg/E3B6EdXFIJBwsCCZdVR3rcvbBXbL4xc/fZ+G49GWjZBMRZmaUkLesnS+88BuvtCtrWkXZQiYmavSkVFNCaLm5zM3K5wXQiAcByFLhGPh4nLJi1PTNOnp6fm0D+O3lmPzRU/BsjimEIJMzGRTXpm5a0ItB3+p9/zF2e3LXqsLQYetoqUAexAESJ4Zkhyck2xIwp6cQFumpgIJbW02un7mfabTUUxLny9gUo0Cmo7P808dxm+4zM06i8v01UqTv/+bt/kX//J6sllrVR7n2VyiAiQrMwEkR4uwOc2KY5oQWkYAAJICSURBVD5fjhYlMw1IWkru1ltQdCVeoPJMJUuWWJpQOav9CUhHBV65wdHBOWZn6gA0HBdtvEoqHcUwtFWuA6czVmxwWSZAE5JdfZ+/QqgFwnkhBMJxELJEOBYuLpe8OF3+A3umPMeQD4eUkpavqu8BbtzShusFRAwNY5mZviYEMePjLZzRENy9TuAFkg9mJT8+KumMqaV5L1DLz+tySWIxi1KxsSp6Go0aJJIWmYyNLqDSUuNjbLyC3/RWCNMFHKfFkYEpdu3uZsDR6IpDewRGa9AdVzZMZ0IXyiIrFYGnhyRxU6zZyvR82JoBWVTHnLFUy9WdkdV2Va1AMlpV0WaAtAx47ZVBEvPWTwuHG0hJudQg22YjhFjhOgDg+QGT5SYtXzI23760M2V9rtuXhvNCCITjIGSJcCxcXC75+LTrupw8eRLXdT/tQ/mtQwjBprw9HzeFE1M14hFjhTBd4M4+wfOjkqOlj1ekGppgT17j25sFt3QLqq4yi2+PQGBp3PuVHeTz8RVL1NGowT33bceOmUQiOsVGgB8osVYsNFRLzzPkAhQKDWZmamzM6pwswy+GJBP1leb9p6MhuL1X8MyI5NlhVU1/S7dY4SF6YQi2ZQTX5AWb04KeuHIY8ALJtCM5NCd5biTg+RFJ2VVR061ZjdpMhcJsjWQqioZckWQQSEmr5YNY6TrguD5vDZY4MVVjeLZO4HskIjpbOuIf8tg/G4TzQgiE4yBkiXAsXFwu+chpyCdLf5uN60uG5xpMlZv0t8eIWavzENMRwdc3qUKbhKGq2D9OIvPL97tzpz2Rj/Ht7+5marLK3GydRDJCPh+n5rQwdA09YvDKRMA1eSUwU1kbzzvzIn06HWVqssqmrW3c0Hn+eaSZiOCrG6DiQs5e+z0BEu08thdIiSfB0mCgAO/OqE5WhgZxU5C24MqcwPFhvAZHSrBZb3Di+BxjoxWuvroXw9BxXQ9d1xZPwfckVnKl68CRySqtlodAYumCZNRkR3f8c9sZKiQkJCTk0ycUpyEXFessOZ6WJrilG54ZllzXAeuSK19bbErmGrAp/fEJH80QaJogGjXo7E5SrTaZmakRiRqIlM2LM4LxuuTOfo1qS5LMJYnHTKqV5qptRaMGsbhJJGLgBefRi/Q0Irogcgar1LGaZLgKN3Sufs7xJK9PQsNXhWCWrvJMLR1sAzalBGlL0vSVID1VkQxVlPH/hhTktYAnHz3G+g1ZdA1ee22Ye+7bzpNPHMZtLgnUWNxY4TpQrLco11sIJNu74nSnw8r8kJCQkJCPTihOQz5xKg0PEOi6tuaS/nJSluCrG+HhExIhVCX7AkeLqgXnQi5mzVPRu1lHFSxlztDS9HhJEjWgP6mja6rrUTAvHgMglY0QiRrMTFexTI14MoIXi+NGDDZH4apUwOCJEqNTddb1JvjaN3fyt//32zQarcV9RKMGe/dt5523R/j2/bvPGl29UAIpeXVC8rWNq8+v0gx4aUJ5mN6WF6vss9RSPtRa0BFTPe+vzovF/FchBMMnShQKDjuv6CISMZiZrvH6q0PcvW8b9VqLaqVJtj3G9h157ISxWNw1WWrMN5clFKYhISEhIR8boTgN+cTx5itnzteQ3dJUB6NHTkp+d9uS2Cq3JDvblv4968BEXUUIEbBgqRlIyWgN+hLK63RzWlApexw9OMfcTI32XJze/jSxuIHPUs/5zt44xUnBBy2bNqmx3Yb6dJ1fPH6Ias0l8CUfGBr33LuVP/zj6zkyMEWhoDxA7ZjJO2+PcNfe7YxhoFckXXF1Lmfig7mV5wPgBxKl39Xjc03Jk4OSL3QpD9KVSF6fUrmwe/Jr+7oamqA7vrS901mw1AL44MAEe/epiOn0dI0nHhsgmYrQ3ZXk6uv6VghTAENfyEyVzFZd2hPWGc81JCQkJCTkfLnkxallWaxbty7slfsJUai3qDZ9JBobc7Hzek+AJGEKEoak4Uui86kA65OC1yeXIogJEwpNyfqkYMaBdQn1fgnMOPDOjOSavMAs1fjJP+yn7rQQQgkyO2py/4N76OqJI4GG43NqpEp5rk5nKmBHV4pmrcXjP/+AmekawUIFkICH/vEA935tO1fs6WZ6usbUZJVo1OBb39lNMmlQ9iRHinBgVrI5s7bXatmVjFRXi9OHjku+s1kw7UjyMXh1QvLlfkHHmnmogjv7BTXv/HJR17zWcqntamHO4d33J7j3KzuoVZqL5vsbN7eRaY/in1YEtiEXY7TgKLsq16f9Qx3BZ5NwXgiBcByELBGOhYvLJS9OhRAYxiV/GT42VORPCaWWH3B4vAYIMnGLxHlGTt+YlHTYsCmjOifd1KUe35IWjNVUgc/lbdAWFVyZh3enJZWWWq4G5Xt6VV4VPzWrHv/jH/bTaChhKqUSZDWnxY9+tJ8f/NH1zJRdnnx8gMDz8QKI6pCNbMVt+iuFKYCEVivgiUcOc/8DUTbtaGPz1jZaQYDnBbRQLVX35CBhSs7UIClhql73ErkY8Sw2JekIlFx4c1rSHxd02mcSpkvEP2TbU1D2KJ3zbVfrTY/xaYeXXjhBIhHBtk2KBYfLr+hcJUyBFUVPJcej/0MfxWePcF4IgXAchCwRjoWLyyVvJdVqtZicnKTVap37xSFr4gWSo5M1Xj5W4MWjBd4dKvPqiSIvHyvS8AKEprG18/yshXwpKTXh5QlJ0pAMFCQqFqq4tQfihqrqf35UUnGVDVVXDF6fXJnnaeo6E6OlxdxQgYqaLpjQN+otpsZKfPDWMHrg4/oSW/NpS1v4vlwtTJcfZyCZnq4iA3Bcb80c081pjfbomZbTld3T8dLSYyfK0Gkrb1bXh5MVybWdAl0Tn6ivXsTWuWPfdjyhIyU0fKhVXcqlBrd+cRMR++yRAonGbPW36/sTzgshEI6DkCXCsXBxueRvA4IgoFarkclkPu1D+dxyeLzKdLU1b7YvKDqq3ZFEwzQ0dnQnz7uNpS4Ee9cJpp2Ad2dUhPGFUcktvcoPVCDYmoGtGUGlJTleVF6i13UIphz45UjA7T3zPeI1mJmprdj+gtQ0BCBgdrqGZVv4EtIWBL5EmAa1WpNk6ixFPhISiQi+G6Dr4J/5lWdkTw6eGpJsmXcfOFqU3NknOFkRXNsBPUbA8IkihTmHbNu526B+WN6YCnCx+YPf38PIaIXxaYd8u83G/iTRc+yvLW5RrCnnglMzdVK2AQgsQ5CIfH6nl3BeCIFwHIQsEY6Fi8vn99cj5DPDzLwwzcQtejJRCvUWhqZalKZsnYYvqLYkMeP823HmbY27+qHYDHh/Fh49KblvPSuKgpKmWta/rE1wqChwfcnONnh2RPKlXtA0yOVWGuwLqZbSg0At07S1x5g5PE1MF0jAk1CteQggl48TiRg0m96q44tEDXL5OI89fJDrb1xPV0/8ggVqRBdEdInjSSK68iFNWjBWg804PPT4APWGv1hZb1k699y3g/YOe1EwLk8L+DAcmJMEUsynTuhs2Zol1ZfhrSnJ4LTkjj6JMR+51QSL7VgX6MpEKdZbBFJyaraxYttdKYv1ORv7PG9MQkJCQkJCIFzWD/mI1JreYjRyUz6GbpqURYzRls1rMzqPnYIXxyQPHZP8zYDkmaGAX40EHC9JJGsvmS8nE9G4tUfj6rzgsVOSuebSezSg5fhMDxXRT40RLxTJEnBLt8bzo5J6y6e3P40dVW00pQTPC/B9SRBIrIhOV3eSsdHy0jaFhlttYBg69brLvq/sIHJaBDAaNbj3vh3oumBosMhPfryfem21gD0fDA2KLsw1VJQ4ogt2J32eevwwtYZP3QMfda1c1+Oxxw5RrfkUmpLXJ1VHqY9Cpw3XdcJCNb+UknwUrspDtQVvTqlrPHyyyLtvjDF8skjL8RcnjlzC4sbNWbZ2JYhFLQzDQGg6Eo2JsssbJ0vM1cKOKiEhISEh508YOQ35SEyUlfCwLZ14xOCDOUnSgu4YRAxBWwSYtxsaqiibJxDUPPjxUcmWlBJCq22SVtKfEMRNeOSE5Kr5aGltxuHJxwdwXRWzdHzJa5bON792GTd32zw/GnBdh8F3HtzDQ/+wn2p1yTjftg3u/+5uyuUGtVqLVEojCCSlYgOB4P3949xw4zr0mMbd926nVnUXq9cz2SixuEmp2KBcaqLrgtGRElsuyy/6p54ve9oFvx6T7GrX2J7VcHxwZqtUGh6OpzxZK666gr4Es+VzcqTCVCLNjgxcE/touaj5MxRbddqCdhv6Aocf/f0ATcfF8ySGIYjHLfZ95bLFCK6ha3Slo1iRCAemW+T0JrOVJiAIpKTR+rgTEUJCQkJCfpu55MWprutks9nQHuJD0povBKq7Pm+eLNCVidKbiq5RwCNYl1R/LnB5GxwrwvOjku1ZSX/i7IH8tojgf7tMcKIiGC94PPvwIfTAX1zWjupQdnwefvQQ3/vdPdzcrTFek+RzCX7wR9cyMlRidrZOW3uMXD7OS78+wdycw957tvHqy6cIAonvBVgRg7rTAk3QlrWJ2gamqRGPm9gxiyAIiFgGTz1xEFAR2dmZOts1uNDGUB224IaMT22qzGTBge4kbtmh6atIqrFGGoSsOXz5siz+GYq1Pg5agWRjJOC5nx1icrKGhlz86KoVl8ceOcgD37sS09aZcZSv7AfTLVJ+BWFKBBJTh62dcfKfQ//TcF4IgXAchCwRjoWLyyUvTg3DIJvNftqH8bmlNxul2vSpNn0aruTkVI1mK2Bzx+rqfMeTTNZhrgkNT+V3TjsSP4BXJ+B9M6AzJsjb0B5daZGkAU3HZ3Kigld1EaaObPk0ENi6yr0szkcYCzWfQ4MVTkTS1FuSnTlofDDBsSPTZLIxjh2dWbGU/+rLg9z71cupVZsMDRbp7k7Sno9TrTR5+GcH+MItGzAMDcdpUas26O5J8+KvTzA1WQVACGhvj+FfoDDVgNkphycfPoTr+rg+pJIWV+3MExMSQyix3vAljWViNZO1+aR06XBV8v6sJECwq1VhZqqGlBIplm4rAimZmaoxPlZh0E4z5UjiskmyVSMdAUFA1NDY2Zs478YLnzXCeSEEwnEQskQ4Fi4un89fjo8R3/dpNBpEo9HwjuhDkIwaXL0+RaXhcXCsStMLqLvLS4MkzwxLZudzKjenBElTLftrAm7uFujz0cGmr5b+x2vKwF4TkraI4LKsoFWs89Tjh3Fdn1w+TiJhUS46xFNRXKFhaYLssuJ6rd7gOzuztPwATcDxfJwXfnWCkeEypzM1WaVUdGi6HrWay8kTc+iGxou/PskNN62j1QpotXwiloHQBA//8weLwhTAjpl0dacA5f15etHQmWg6Pk8+PjBvYzX/WN3FjkdoNDxiMZOGhHpLdb8yhMCydDq7kue1/bVwPImpqRsECTQ9cH3oScBQBY6WJHv7BXFL5/WXqov+poFc2WPKDyRTU1WuuT5LTJccnwoY85Uw1QXcuDnzoY7vs0I4L4RAOA5ClgjHwsXlkhennucxOTlJb29vOOA+JJoQpG2TXMJitOiuyB8tu+B4sDkF13au7v2+nIiubKIUyu/zZBmqNZ+f/2wAXfoYAhynRV9fGikltXKDVMbG9UF6Pr6vmgAkEhZDw1W0WJQJT7CtO0XUNqmvUZxj2wb5jgTHj84ydKpAudzk1i9uolxq8OjPD7Jxcxtf3rsF35P8w9++Q6OxVPyUSFp853d2MztXo15vMj5WOS/bJyEEkxMVXNefz8hV/1k6vLV/nL37tvHCr08iEGQjEn1emN5z345z2kkFUqIJOFqEw0VJOiKQUgl/iYpKd8UEhlAFWREdXpmQmJrgvvVi0VEhmYogpXrPgvXW0glAOhUhZQk8X5JLWowVVBvU82228FkmnBdCIBwHIUuEY+Hi8vn/FQn5zGDoSmYtpElKJL8clnyhR3U6uuDtaYJtWcHwiRJR4dMIVBSxWXS5KhlZtHkKPJ96vTVfiR8QjRg0mj5uy+Hoa0PcdPMGKlWP7/3eVTz95BEKc3XKZVUcZdsG9z+wh/feG6Mjn1x8/M3Xhnjw+1fxo797l5PH53j4Zwe5+95t/OEfX8/EeIW52TptuRg9PWlefeUUuVyC1w5PU6sq8buW7dNylve0V9dqyTqjVHB47/0J7vv65fiuR2GuTiZ7/j6n70zDQEGSs1WDgqavIrrHS5I7+wXrEqs/i83plY9V3IC2jiTRqEGj4eHLpSYGoKy0OruSi3mvE6UmAokAdnSdX8OFkJCQkJCQtQjFacjHRrHuAQLbUneVAsFXN567Ev9sLIg4DUFMh6imLJXeeGOEvfu28/QTaqlfCAj8gGjU4O5923n9lUHKlSb77t3BU08M8PVv7mJkuMhll3cQT1gkU1EajRaJRITXXh7kmuv7aDS8RcH7+msjSAn/+79SYnRmuka91qItG0NKtcwfs02efeowIyNl7t7XtihMAVxXLdl/54HdmGt0WFre017Mx04tXUWZdQ2mZx1mKy2uuKyNdZsyBBLGqgHvDAbcs06c1S/2mg5BzICWVK1U7flv+b0b4IlTknvXQ+c5qvxtHSJpi/u+soPHHh2g5aposR9ALGaw794dJJLWYnqBLph3XJV8MFZlV2/ivBsvhISEhISELCcUpyEfC4GUlBwPiUah1sLSG+RTkY8kTNV2l0Sc5wWUSw0CKalWmrhNj737tgEwOVEhFrOwYyavvzrE9LTqDFWtNtGE4NiRaV5/dZhazSUWt4hGde748lYmJyps2NjGoQOTbNqSW7SNqlabJBIRhgaL9PYm6elNUas2efWVIdLpKPnOJG++MUKxqKr9D34wweL6/Dyuqwq4+jdlV+WILu9p77o+lgamBjUPLAHC0MnkE1Rcf7Ew7K1pyfWd59fIIB2BY6WVj3Xaglu64YlByQ8uO/c2dEsjkYvzla/toFpuUio1SKWj2MkI7Z1xDEtbjOJuzMeZrbVwW1Bt+rx5qsyO7jiZmPmRx0BISEhIyKXFJW/CL4TANM1PtHf5pYAmBOvaomgioOq4HJus8srRWY5NVj908Q4siTjT1BaF6QKFuTq/fu4EuqExNVXj7bdG+MXjA0xP1ZABBIGkWGjg+ZJisUFnVwKAcqnB7IzDL58+gmUZvPTCSY4cmeGdt0YQAvIdcdIZm0hEJwgkP33oAD/8v14lnYmyeUs77bk41XqLXVf3ce9XL2f/22MMDZZoOh7BaWX0xYLDmbRZxFZL/5alL2paS4NIROeufTs4VNOVT9U8DR867PP7ynbHYXN69eNbM4LeOBSa5/pMJL8cCfBiUdatz9DZnaB/XZqu7gR96zLsb0R4ZTJYbKSga4LrNmbIpyJINLwADoxWeflYgZnq58+EP5wXQiAcByFLhGPh4nLJR04ty6K/v//TPozfCjblY7QnTKbKLrPVFg0vYLzYIGLq9M9HPy8UDTAMjTvu2spPfrQfv+ktTg6GqXPv1y7juWeOUq+3KJcbS3ZH8yIxlYpQLjmkdnYwPFQkFjfRNI1m08NpeBimxpfv3srUZJWueQupXz51lFKpQb3mUi43sW2DB753Fa+/NkxXV4psZ4Lsxiyy0ODY0WmOHZtdPF7VEtXGnF/SPpvtUwC0d9h854HdHB2q4FYdUmmbZC7J4brGcElyx3zUVCLRL2BOFCgRuhY5G2YcSTZy5g2O1FQ6htqGTve69Ir2pbfHJe/NCH4xKPliL8QMlWqwoztJX9bm+HSNUr0FUnJwrMpNmzOY+ufnXjicF0IgHAchS4Rj4eJyyYvTkI+XtG2Stk02dyhRMlNtMVNpfihxWnChPlvntVeG6O+K8+W9W6nXW5RKDdraYiSSEYIgYHamTnze6N335yN5uqpuj8VN3KZPJGJQq7pUKqrgKZmK0Gx4DJ0qYJgaHR0J3nxjmC/dsZm77tnG+FiZ2dk67bkYvb1pGo7LFbu7SSQtnpoQfMls8f/9/73Bl+/eupinCkq4zc055PNxovNFQ2eLHAeAaes02zP0rs/SEZX85GiAG0i+umFpCb84b8X18SCotFY/Ol6XTDsqWGtqkmITnh6SXNupOn35cuU2ducgZcELY5LtGdiYUseaiBrs6U8zVmxwfLJKICX7hytcvT51XikJISEhISGXNp+fUMYnRLPZ5OTJkzSbzXO/OOS80YSgK62MRy/UMN4NJI+eCmjUfJ5+fADfcbEMnSceHeDNN4YZHy3x5uvDvPTrE/zy6aPcfe92dF2QTkUXnQJMU+ee+y7j4IFJ9u7bzquvDCm7pvljKRUbWBGDVDrK0KkCruszMlTmkZ8fYnS4hK4L+vrSxGImpUqTRHuCdM5mxBUkTI3RkRJOo8Vrrwxx973biUSW7vNkoKyc9n1F2T6Biqjqmli1JLTweMJUBU+gCqKSJrwzA88MB/zj0YBXJiVX5j6asFvY15U5wUBBMtdY+cGYQtlKuT40fcG96wXbMoJnh8/8AW5ICb7UK3hrSjJUWfm67nSETR1xJBrVps9M5fOzvB/OCyEQjoOQJcKxcHG54MjpwMAA//E//kcOHDhAJBLh+uuv5y/+4i/I5/M89thj/Pf//t8ZHh6mt7eXf/tv/y233HLLJ3HcHysfJScy5MwkowZCQL3pcWCkzK6+1Hm9r+nB9Z0arYkiuh9QLjawLB3D0CgVGpQKDQxDI5mMUCg4vP3mCN/49i5Ghku4TY9kKkoiGcH3fL54xxYef/QQU5NVkskIhqnhtXykVOkCmUyUocESjuNx973befoXh3nul8eQgSSejJBKRbj+tk1EDEnDl7w4Kvn9nRovH1UFV1NTVV59eZC9+7ZRr6mobjodpX99mlRHAqRPa76zVWHOWfRAjdo6jWWP22mb9R1J5po6KQvu6v/47huXd9daOIa7uxK8PaNzW7fEms8XyNmC3IoAt2TS4ZyiOKILkpZcdRMihKA3azM06+D7AeWGR0cqsvZGPoOE80IIhOMgZIlwLFw8Lkicuq7Lv/gX/4Lvfe97/PCHP6RarfKnf/qn/Pt//+/5gz/4A/78z/+c//pf/yu33347L774In/6p3/Ko48+Sk9Pzyd1/CGfYSKGRj5hMVVxqc4vewdScrykzOF9qQqAOmMqYmdoglNl9XhXXEPOOHi+WrZ/dT5C+dQTh2k2PTRN0PICbNvg6mv7eOLRQ4Ba1tc0QRBItmzLMTs7xjXX9TEzXUPTBbgqemrHTe65dztjoyWklExNVnn7zRF+7wfXMDRYZHamTjodZfPWdvLdcTw/4OAs9CYESEkuv5TQOTVZ5dGfHySVihCLWxwZcMm2bWeuIIlV6zz5+ABN1yc+3360ozPBtdf38+xTR3DnW0MVXEl7XOdL9+zg8rbYR7jqkuVu+YstUh8fWNwXgGlp3Hb3DppBDOs0x6ejRUkrgJILcRO2Zc4uTifrKlK8IbX26yxDw/Gh0vDXfD4kJCQkJGQ5FyROHcfhz/7sz/jmN7+JYRi0tbVx11138Xd/93c899xzXHfdddx1110A3Hnnndxyyy08+uij/PEf//EncvAhn33626JMVdzFqFqhqbpG7Vuv2pbWPcmhOUmtBdmoameaMAVCwGDJptHwFsXj8ghlrebS25/Cbfq8+vIgM9M1sm02c7P1xaX7XVd0MTpUYna6zj33bScSNRg8WSCdjqpuUXWXDRvb+NKdoGkadszk4Z99QMNpLYpMPwjo6U3wxoRk2lEm9r4f0Nefxo6aOM5S8ma53FQFVDGT3r40Rb/FI08MYEgfzYBqCwwhuXl7np/8aD/xuKr8dANV7NR0fR5+dIA/+v09wErF2Aok78/ClbkzW0kNFFRr0uVV+gstUl13pTBsuQEvPnWY7zywG07zI01aUHGhK7aUR3o6789KDs5JdA1iOnyx78wCNmpq1JsaJcfD9QIs45LPJgoJCQkJOQsXJE7T6TT333//4r9PnDjBP//zP7Nv3z5c112VT5dOpzl06NB5b19KuSqfQ9M0TNNESonrrs5Zi0TUMqHruqtC7oZhoOs6vu/jed6K55Zvt9VqrdqvZVkIIWi1WgTByp48uq5jGMaa2xVCYFmqOGet3BTTVNXinufh+ysFw8J2gyCg1VpdsbJwrp/Uds92Ddfa7rk+G8uysAwN3/do+ZJ6o0na0NjTBkLqIARRTbKnbeV2PU8QjVpkM/Z81ynF8ghley7GjV9Yx//n/3yVRkOdUxBI0ukopVKDSMQgFjMpl5uLLUJNQ+WKjo2WmCs47Lt3B7947BA337aJp39xeLE7FLD49/b2GJWqR7nps3edjpQwUnYpuDoPfv+q+XamS9fUtg0e+N0rOXBgnHw+Sc3xyEQFhhCYloSISancpFZvoZsaLaHjS7ANqLqSpOkxOV6md31StSHVDcbq8PZEiyvaIPDForeopulomkYQBNRdjyOzknvWCTxPIISGYRhMTFRoNltweq4r8z6s4xV61idWWHR1RDW6YjpBEOB56rObqCtBek2HoIXJZF1yb5/HsyOSW3sEFgLPA103EEIQBP7i96Y7ZTJVctAJaHoBhsYFj8Ozje9PYo5oNpuL/z7b+A7niI8+R5zpGp5t/j7XNTyfz2atcz19u81mc8Xvw2/TNfwo2/20x/enNUecrhU+z9fw05ojzpcPVa0/OjrK3Xffjed5/M7v/A5/8id/wltvvcXf/M3f8Oyzz3Lbbbexf/9+nnvuObZu3Xre2/U8j9HR0RWPJRIJOjo61nwOYNOmTQBMT0+vutj5fJ5kMkm1WmV2dnbFc7Zt093djWGoSzA1NYWmLUV01q9fj67rzM7OUq/XV7y3vb2ddDqN4zhMTU2teM6yLPr6+gAYGxtb9SH19fVhWRaFQoFKpbLiuUwmQ1tbG81mk/Hx8RXP6brO+vXrAZiYmFg1cLq7u7Ftm3K5TLFYXPFcMpkkn8+veQ2FEGzcuHHxGpz+Bevo6CCRSFCr1VZdw1gsRldXF0EQrPnZbNiwAVMXVCsVfKkxMNikN2PNH1OKWCxGs9mkXF5yi/cDyVzLZF1PFxNjZe772uU89OP3FqvhQYmqK/b0UK81+e7v7uEf/+FdGg2PSrlJMhUh3xFn7z61ZL933zZs2yQSNXjmqaN4LR+E4O57trF//wh+AKWis+rYQQnN7p4U7+0f5/L1aTTiuH4LrzJHtWliJG3+9391varsn1GV/d3dKd58c4ihwRJcDlEtwPc1NF0QBAHtMZ1y0QEkXitAmoIAjVoLdOlTc2FovMphN6DacKnbHViGINIoYzY8Zpd9PKlUCtuO0Ww2eOl4ia1JQaGgnjNNi0x7O6OTDuWGT9Rghd+qYRgIYHa2SiIbUHZcph2JBqRTCdpSCTzXxa0WeXtako1Ajw6vnNSJpHNsScErJ+bYlYR6GRa+Ie3t7RiGSa1ao+6oR0eLLuVSg4ipEY/kcJtNxsbGVlxrTdPYsGEDAJOTk6smv66uLmKx2Jrj+5OYI4IgIBKJLP7wrLXdcI5QfNQ5QgjBzMwMjrPye7hwDev1OtPT0yuei0Qi9Pb2Aqy53f7+fkzTpFAoUK1WVzy3cA0bjQYTExMrnjNNc9EuaHx8fPHHdOH3oaenh2g0SqlUolRa2eUilUqRy+VotVpnvYZrje/Ozk7i8TiVSoXCwpd4nng8TmdnJ77vr3muC9udnp6m0WiseC6Xy5FKpajVaszMzKx4LhqNLqbcrbXddevWYRgGc3Nz1Gq1Fc9ls1my2SyNRoPJyckVzy2/hmuN797eXiKRCMVikXK5vOK5dDpNe3s7rut+5uaIhXG/XCss6Ihwjjj/OeJ8EfJDZvhKKRkcHOTf/bt/Rz6f5z//5//Mj3/8Y/7n//yfzMzMcMstt9Df38/AwAB//dd/fV7b/C//5b/wve99b8VjFyNyGt7xfLJ39K8dm6Xc8DEMgxs3ZxBCrIj6BYHarh9IHjsl6UsKLu+IMnVkGk2DRsOnVnUXi40WukDlO+Pc/sVNtFqSmakqs7N18h1xuntS1GpNinMNIlGDtrYYc4U605NVMlkbw9R5791R5uYcBILdV/Zw+NAUg6eWfhQWvE0PHZoEBI2Gyx1f3ooR1Wh5LQ4UDLzDI7z4wkm2bG2nszPJ5GSFw4dn0TVIJCN88UubeOk3gzR9iBsCiSSesNi2Nc+zzxwlkYzgaToLqZhRXV37m+/YyhEjzsakZE+HqZbxA58V7adYipwOlX2Oznnc3rd0cyURPD2qs7lZ4tfPHsHUVqYDiPn/33nXFnrWJyi7AUeL6rkADTSdlh9QdDx2ZMViPumrE5ImBv0JQb/dwjzNfFXXDdwAPD8goqnvzUSpydHJKjqSW7e3o4vPfuT0fLYbzhGK3+bI6en8Nl3Dz/P4DueIz+8c8eMf/5h/82/+zar3n86H9jkVQrBhwwb+7M/+jAceeIC/+Iu/4IEHHuCBBx5YfM1/+A//gc7Ozgva5sIJXshzwOIHuRa6rqPra/f59n2fSqVCJpNZjKIuxzTPbC55tu0CZz1ewzDW3B+oAX+2935S2z3bNTzbds/12aTjERy/RToewTTVPiQSP5A0AkEQGJRcOFmWRC24oVsDAqz+NP/0j+9x9bV97H9nDMPUODLgLuZ1fv1bOzFMjcmpCkIX5PJxYnGLkeESp07O0mz41OotLFOj7rQwDY3rbljHL58+imlqaPOhxLZ2m298ZycjQ6XTIqDDvPn6CF++aytHp6qLrUgHyia5mIY3XxR17Ogsx46ujBhVK006u5LEogaVikccZYxfr7ZIJCPYURPdUJ2hDKkKj0pNQTau09OTYrosaEqwFozrtTN/VQ8VBdd1WRjGklA8OCfpT8DmjiTv2OaqnFMAy9Lp7E6i6ToZG65bZUWrszz39VQ5wAXuXBTBq8eLRPLmlKQtqnFZVr23I61zcqaBIMAPJKapfyrj8ELmCM/zKBaLi/PC2bYbzhHn3u65PptP6hqebbvncw1PHwcL/DZdw0/rs/ksXsOzbVdKeUat8Hm8hp/WZ3O+XFBlwiuvvMLdd9+94g5gIbztOA6PPfbYitf/5je/4aqrrrqQXVx0fN+nXC6vuksI+fhYyEVeWlaWvD8r+elxyQujkpcnJFN1yfas4N4NSwIrkTDYe8923n5zhJtuWc/OXV1s29HBXXdv5V/80fXE8jFer5jkejMkk0qcRW2dfEec2Zk6R4/OMjaqBGdhzuHKq3v5zYsnKRUdfF/SqHu4zYBcPs7PHzrAsaMzSCl5f/84/9f/+TJvvjZCNGqQSEaoVd3FVqQ722BdQtI7XxS1FrZtkkha3HPvDqKnlcMfOTzN/Q/uITB0mr7yNDWFoCOpqvXTKZ1JB27uOZevqeT1yYCkCW1RgeNJhquS34xLJuqSXW0rW6Qux7LU4ws+rBVX8sakKnIKpFxsS7pA05e8NQU3nONeMwhgsg7bMkuPzS5rX6qfqZfrZ4xwXgiBcByELBGOhYvLBUVOd+3aRbVa5T/9p//En/zJn+A4Dv/tv/03rr32WjzP48///M+JxWLcdttt/PCHP8RxHO69995P6thDPidETQ2QVBoefqAqvJGCr2xUbS/PRAvo6Y3zne/uVh6mrs9lOzvI5eJMolMoSG7pAl3TEaml9poa8JWvX87J43Oqm1R7jN6+NE8+PsDkRBWhCRynhecF3HX3NqYmytx59zYe/ucD7H9nKUcnGtXZe892Dh5QeWkLrUj1ebEdixvc/+AeHvrRfpwVRVEm9z+wh0hUx4rafO13duMVqpSLDpmsTXtHkvdqOl/+5m78YpVmRT3e2ZUkYusEwOVtggOzcEX72tem2gx4bkw5H9zWI3h/VnKqIlmXEGzPQM5WN43LW6ROTlQoFlbvC2CuqZIGjpclB2bBDaAzpiyipISaBzd1C+Lm2cWloQuytsAQS0kIrr90M1t2PNoTZ76zDgkJCQkJuSBxmkwm+R//43/wl3/5l9x4443EYjFuvPFG/uqv/orOzk7+6q/+ir/8y79kdnaWnTt38td//dfEYh/FszHkt4F4REXnWl7A+yNl9vSn2H0OY3chBJoAT4Kdsti1J48QBq7v4XkB/UB/fGkbUkoCVAckXRMMDhY5enQGO2YyPVXlxLEZ9lzVw2U7O5mdqdPdkyQSMXjh+RM88L0r+dlD7/Otb1/ByRNzFItLua0vvXgSJGu2IvWBrp44f/ivrmd0uMTMbJ22thj9/WliCYOF++v3Kxp3bsywUc8yWpX8dDggogc4UY2dfRm64tnFnvULMm53Ozx8UnJF+8rrVPMkAwUYKjMvYuFEWbURvW+9WNNmaqFFav+mLOvF6n0BrE8K1idhuUfqhbBg9D82XkGbcBhq2nTNC+D+NpvBmToCmKm6oTgNCQkJCTkrF5xzun37dv72b/92zee+8Y1v8I1vfOOjHlPIbxm5hMW2zhhHJutUnBaT5SZd6eiarz29m1E6Y5NKRRgbKRKNWfT1pUmmLbxALoqshfdMjFWYmqrS1h4jnYpQKTeZm6njeQGGoTEzXSeeMPE8SVd3gqd/cZiNm9oYHS0xMlzi4X/+gNu+uIkjh6c5MjC1aCfV1ZVYbEUanHa8PhCJG2y5PE9tVrI+rfrSL1/4qbdAF5J3piQjNcn9WwSGBs+NSCK6xD99o6iGBElTcrQo2bpogi/59ahke0Zw5SaxGME9X6RUDQ7OBz+Q1D3lQCWBkSpcll17f8uN/scrHhpw2ICkbXD3fdspLXtb0ws7rISEhISEnJ0PXRD124Ku66TT6bMmJId8dHoyUWarLWZrLWar7pri9PRuRq2WT2HOwbJ07r53Oy8+f4Jy2eW7D+4hl4/htjwsy6BQbPLYwweZnqrieQHbd+S548tbqZSbK0zyNU0QSBtdE+Q7Etx+x2asiM7MtLL4mJqs8sLzJ7jhpnWrWpF29SRptM7s0eb5PtUmmKflVB4tKhF9tAjTDeVDqglVuT9Wg5R1ZoF5a4/gn09IBLAxDS+NQW9csDl94dHNQEoOzKmI7EJ0VCIRZ4iUNgM4OG9e0ApgpCrPKE4XjP6broeUIDQlal3X57HHDrH3q5ejESCA3szno31pOC+EQDgOQpYIx8LF5ZJv1WIYxrw34yWv0z9xlNH76qXnuYbkpfGAcm2pm1EQSApzDlJKGg2Pp544zHU3rKNec/nx379DsdigUnIpFRs8/shBpqdreF6ABG69fROP/PwD7rpnG5HI0ucaBJJ6vcVXvrGTxx85xNO/OIJh6PT1pxZfs2D0/8ZrQ4wMF3njtSGqFRdPrhHeXIYmBDd1rTyvQlPy9rSkJZVIvaNv6dwn67D1HCLTNgRX5+HtacnjpyQbUrDnHOkQa+FLyS9HJMpiVr1/vCbZP33m98QMwQ2d6r9bugXOGXS5EILJiQqu6+PNXyJbB2tepLeaAcWZGlFTRwjIxM5ctfpZIpwXQiAcByFLhGPh4nLJX+UgCHBdF8uyVpjwh3z8+PM9TFWBlIrcPTMk8SREdMHgSJnpqoelC0RLCdQFGg2Peq1FKhWhXG4yPVXj6NFpdu/poVpxCfwAKaGnL8X0dG2xgGrvvm049daKPNJ63cUwNJpNj4d/eoA//jc3EY0aNBpLCmx5K9K+/jSed3ZxuhzXV3mhIzW1JD/bkLRHxYooZb0F56PTdmQ14qbkSEHOX7+1xel4TTLXlJSbqtvUnrzanxsoYbq7XdA7n6Nb8yS/mZB8beP5C92YoW4ult9YTDmSzphQNxFIah4Ymoq6zqcZY2iCYrFBrDeBLiSfk2L9cF4IAcJxELJEOBYuLpf8FW61WoyNja1pKhvy8TFTdSk3fCSCbFwVxAgEe9dp3LteY+86jaDqkLYEUZ01xWCp1CA2/9652TrZbIyZ6Sp+IFmoU2prizE7q7pQTE1WefyRQ7zx+jBjoyXeeH2Ixx4+yPhomfac8ihtNDwGT83x+z+4huhptlB2zOTB711FInF+93B1T+WI/mJIkjBh33p1Ll0xyJ62mj1Rh/a1025X0Z8QfKlP8OsxJXxP51hJ2XElTcGWDBwvw/uz4AWSZ4cl1+SVMJVI3pmWPDsk+VKPWIxuLqfpBQzN1hmcrdNc9hm4gXJCWM7L45JCU5Jus6m0lCjOWAI/gNb8ByKRZLI2rufTnYmuanH8WSWcF0IgHAchS4Rj4eJyyUdOQy4OyzsJDc85pGyDiqvM5w1NEEjIttm0AommCUxDU8U4ywRROh3lyIDyzGxrj/HB+xPs2t21+FoEzM3V2bI1t2Lf1UqTaqW5GIlta48xM1sjm7VpeQHVisvOXXH+1f/zRkaGS8zM1GjPxenrS6MlDM5nKmr6kkdPqkjpvevFYu7p5W3ws+OSO/qWXjtek0w3JDd0nb9Q04TgC90qCnpHn4o0A8w1Je/OqCioEpuCb29W73l+VLI1DbkovDEpGalKqp4Sxal5sVxqSqIGFKpNZqsuxVoLOZ/CMF5scPX6DJahcVVe8MEc7Fl2aZMWDFUk7ekEiaiOtqyyywvA1JWfaro9xsxcBW+tyq+QkJCQkJDTCMVpyEUhbZt0pyOMl5oUay6leouTdZMjRUkuKmn4sDOZINB16k2fhKkjhFi0bopGDWJxUy212wb5fJyjR2b40pe3ELUNqlVBEEjGRsrkvxJfXKZfHqiTqGhoR0eCl144iaYLYqZJZ1cC3w/A0ujdEOXy3Xl8uXb09kyYGnxto8Bew7d1fRJ+NSL5+ibViODdGcmXei+82n5dQvDWpOSXw5LrOyWjNcFkXfLFFVFQybQDRRda88b5A3OS/qTgm5uXFkrmmpK3pgIMAe2iTqmqOjgBxCyNuhvQ8gLGig025GJ02PDUkFyR83pFu2CsBlrU4Btfu2wxX9iXKm0hZWvcc98OBuvK9SAb/3zkm4aEhISEfLqE4jTkorG9K85EqYlECcXd7aq6/VhR0hGDzTmDzDeWRE62zV6s1t+7bzuvvjyIbRvc/8AeXvz1CeyYSSJhctuXNvPi8yeYnqrSagW8+OsT/M6De/jpT96n0WghhMD3JbZt8jsP7GH//jEMQyMANEMn2p7izQLsyfrMFioYpoVhXJiQ0oTAPsO3adKB67sECDhZhg5bnLVKfy3mGpJXJyX7NgiGyv//9v48yK7yPvD/38855+5776tWhCQWCQwGYwMBA2bHxjG2Eyf5JU5mslUmlanyzFRlKnHGyaRSmcoks1ZSM67JdzyxEy8Y22C8EBswNpjF7JIQ2nvf7r6d7fn9cbr7qtUtqVtI3Y3686qikPree+65R59++tPPeZ7PR/PNo7Arp7mpT9HwYF9eU3fh7YImGYaeuCIVhmwYjlfAPGkBz6Gi5kBB875uhefY7B9pJaZ7BlJkYhbPHylSd4Oi+gBdscW39btiiq7ZlqdGolXo/9Bona6OGF09KbIpg9ffqmHAkiWzhBBCiFNJcgqyuHmV2LO76QEajk8mHhSPv20wSNaW6maUzsRIpSKcOFHg/TduoaMzwY+ePMyxYwV+4VNXcfDQNIMDGT75qas4cbxAfqZOMhUmm43yG791HcNDJaYmq3R0JmjviPP8i8McPJTH8SEaMfnovTuJxBSFksb1NUqdv1iou5oXJyEVDtZ8+jrYvX//CjYiAUzWg8T09oFgZjbZDi9NBZuu9uc1A0nojinao7Brqzqp61bw/2vQ/HBI862jQVmrmgNJS3OspLHsJoqgXNX7tmeJWAZHpmo0XB+NQVcquP+vWZycnmyu0H96IIsdytLdCT+b9LktrYiFTZq2T6Xp0vYumj2VcUGAxIFokVhYPRs+OY1EImzZsmWtT2NDCFsGXakwE2Wbt8crdKXCbE4t/GZfqptRWClybTFGhku8fXCKK/b0cuc9O0FpzFSMw46JdaLAT38yRHdnnFrT4diJItPTNRKJEOlMjHhbnEYozLZLOmjvSNDZ3upg5AOb05pX8xbX93TPt0E9uRvUSlVdzePHNDf0KPpmE9PvndBc3bH0RqTTmaprnp/Q3Lmp9bqQobhrE7wxo+mIKq7qPPPxDBQfHFDUXc1MxWY4X6Nhe1TyQdIZ7OvXWLPHPzm1PTBWYVdvcn4G1fb1ovO3/WApQc2B16c1twwEdQmOl4PHLUPRBELmu2dgl3FBgMSBaJFYWF0bPjkVq2tXb4Lpio3WkK85dKaWLsp+cjejptZgwabtWbbuaMPXPr4XzAJGtabTULx8tI6ybUaGbNzZneVKQb5kY45VactGufq9ffi5HMYSLTwtQ9Ftebz1VolGqU6ubXH/+eU6VAwSyrs2KbKRIJF7dRqmG/ChTcs/TskOjnNr/+KEtjeh6Ixp/ultzUhV0x2DKzsgfIYE0HFcDo+XUWiM2TnsuXRUA47nYxomm9pjOJ5muNCkWG2yb0SzZzDD+3uD3f7Xd7fOxfU1/++AZlMqqI167+zGrB8Oa97TOVfsP3iPo1M1ejPvjiL8Qggh1s6GT05t22Z8fJzu7m7CYen5faEZSpFLhJiquIwVm6dNTiGYkTtaAkuBp6HQ9Hi7FLTHfE+noi8JpWaQ/MQzMRwfTAVRk0Wdj9raYvMJ6VzS6/iaN2dgsgE7jTrfeXQfTsMmFrZQShEOm9x17y46u+N4K5hJzYTh/q2KxEmbo/bngxqfZTtYynA2czOmP9e/9CYrAMsw2JHxeX0GKk5QQuqj21szoKc6Nl2fvYWvubw/ScQymKrY+BqSEZNoKChOaijFju4Elqk4Nt2gUHPQWrMlpfiHUc2BQpCu5yIwVYdb+xVb0q331GiOlDQ3XBp8zfODXlS266O1fleUk5JxQYDEgWiRWFhdGz451VrjOM47uoUrViYWMgEH54w7ZDQ/Hg3KHtmzt9kjFty1STFZ17w8qTlWhkxYEQtpujpSaNOkZnu4Ggw0SgWbcGJREyOb5OVJn7AJwxVN3YOqEyS5N7X7fO1LB3BsD1MptA+OGxT9/8ZXX+djn9xLtdYkl4sTiZkM1zTpMAuSz5N1xBZ+3fE1YQNu6FEcr8AVbWe+Pq4frE295QyJ6RzDUPPF7d/brfjBsOa2gSDB9NH8/T5NxIK7NwXJYXBlIWIZpKIWqejph4C52/CR2coJERN+bTeAwvGDJD9ssKAwv+NrnhvTXNGmiJhBtQVn9rcBy1TvisQUZFwQAYkDMUdiYXVt+ORUrL664wGKsmeyL3+6nu2KW/pbf/PRPD8OT49otqQV13TDtpNm6wxMHjppp7+PRgPR2dnPeNrCrQVdjK7tat1uV0px4nAR2/GCGUdPkc/X0b7G9XxKJTiwf5L29jgv/vQtrr5+Mwd1jBt6lv9535iBy9oUvQlFb+Lsz7cMxYc2LbwmntaLSk8dLmkO5IMkUSnoiml8rfj6YY1paBou7MoFHapGq7CzN8lLRwsYaCZK9hkTU2D+l4eFE7Gtda9LbW2abgQF+2/sC55Xbrh4no/CZ0fXMj68EEKIDU+SU7Hqmq6PRvF63uDjvct7TXm2LecDWxevqaw4mri1eKd/Nrdw3eim1OIk2FIwM13HbnooBcVCAyAoGD/bLbSQr/P221Psfc8Ajz66n1/+1F5Chrnszxsx4JLMsp++gOdrfjiiKTZhTwdcklGA5uVJOFjUXJKB4WpQlqvQVGxNK7akg2UNE3VNKgSmoZhpzM1YBxqud/bztoJr7Z9mm/5wVc+3RJ0TMliQRA/lGyg0HckQXWlZbyqEEOLsJDkVq65me7i+gY9BR2x5r8lEFHuWzG00bxfgUEkTMjVoRdTM0LkpS1sWQhan3dBkANOTDdCaSrlJNGoFs3yGYr7mlQ46Ux08OEWt0iQTs5gYq7Bpew7fD45cbGpGapxmBhh2t537rWwP2JFRDKTgq28Hu+KHKprueLAONxEKEtbtmdZ7zK237ZpdXtB0fSrlGqVS6xn+MnZ5zd2Ct5bYZGX7mu8e1/zipa1uVQBvFaB99t+04XhMl4NSVYNty+zVKoQQYsPb8MlpKBSip6eHUOjdU3/x3S5kGIw0fEztLtq4BHPlioAlHltMcVUnC8opjdc0z034vF2Atij0xhWZCPTGF66PbNY9Hvna61x73SDhsNnqCHXSRGE0ahGLhyjk65QKDUzLYGiohGuaRNMxzKjJD4c0dwxemLWUYUOxKRX8+a7N8PIk9MRhqhGsI2368MKE5sr2pd/f8zX7R8uUas78Ln3LUHQtY1dW3Q6WX1SbLkenamzpiM8/ZinoS8A/vBWscd2UUhwtaYarmo/OtmUdmZ2FNlXQIezdRMYFARIHokViYXVt+OTUMAzi8fjZnyjOm2jYoC2mOVpY+vE3psHx4brulR33UMEnGVF0xxUPbAk239Q9KDag4sFLU3BZTpMMG1gmDB0qMDlR4dkfH+OOuy7lqR8eplZz5lueRqPWfGcqNCQSYYZOFIhGTJ5/+hCNpscH79rF3ZvjJFdhvGq4QWH/93QqfjKuycyum02HNUdLesGO+TmtxNTHVLCjO0FXOrwgST+ddMwCGhhoTkzXKNUdBttieBoc12d3wgff4nglRF9C88OjdbbGmhwYsejPRRnJB52nut+F5aNkXBAgcSBaJBZW14ZPTl3XpVQqkU6nsawNfzlWRTZmUai5+HaTQ8UE2zMLbxvv7YC/36+5rntls5HDNagWNckQ7E15lKcqTE/XSSbDpNMRQnUHR8UoWYpivs74bLvTifEKzzx9lI9+7EqOHJ6hXG6STEaIx0M895NjTIxXiEYt4okQzYZLNhejVnEAeOo7B/jYJ/dAaPlrUFeqbGtKDjw/rrlnS7B+NB1qXZv3dSseO6Z5dTqo/XpZm2J7Jlj7WW64KHwiluK6rVnMFTQA6EiGuXZLmqNTdaYqDqWazeu14HOr2enlqFbk7Th/N+TRYTUwfZguu0yVm8HjluKSrnffgC7jggCJA9EisbC6NvwV9jyPQqFAIpGQgFsl8XCQyO3tgDenPcr2XJejILlydXDbeLyu6Y4tP5l6f4/iSEkRqdb4xy/uR9kupWId1/UJRyzuumcnT/3gCO+9boD2zgQdna3d4xPjFR55+A1uvmUbh96eZuhEgfGxCr6v52dQX3pxiNvvupRjR2fmX2fbHuNjZQa35S5YiZGhKsw04P29QYH7uqs5eTLSmu0Y9a2jQV3U8Ro8Mwo39+n5hRGb2mIrSkznJCMWV/SnmKrYjBeb1B0f01CEzKA0VanhkdU1smnm26AaKqguANCbiS5rlna9kXFBgMSBaJFYWF1yhcWq60iFMcaqxEy4os1lX8Xkeyc0O7OKo2W4uS+YiKw6wDI3TEGQpG2JeHzl4QOEfI+ZYgNndh1ps+HynccOcMddl/L4Ywe478OX0dObIhKxaDZdIEhQn/rhYa6/YROu46H3BrfyE4kwhUKdq9/TT7PuMD5WWfC+hXydzSo3X9z/fNudU9RczUuT8Oa0puJqPtCzMOELm4r7tga75dujCtfXvHKihO36KDinxPRkHckwHcmF61S11uwbrTJRtolaBgNtcfqyERRQd4LrPveLiBBCCLFckpyKVWcoRXsixGTFZSTf4LatMV6fCYq3f6BP0fSCPUnbllhDeSZKKcbHytiOh+P4wW76kxLGRsOlXnMIR0yKxQaxuMUDD17GNx5+c0GC+r3H3+L+j1xG/0CGHz19hGKhwfhYGc/T3H3vLqoVe8H7ZnNB9ykIWpdGTBhInv7cC01Nw4Oe+PI/n+fDrixkI1B2FLnI4tee3OJ0umLPrzXNxS06Uue/o4lSisv6klzqa0zFggL7kpQKIYQ4V5KcijWRjFpMVhxqtofWQUeht/KaiAFPjbT6sq+EoSA/UwfAc32WusteKDRIpSIU8nUy2SipZIQ779lJtWJTLDbIZKIkkmFMS+E4Hi88N0RbexzH8fnQXZfy+qujC44XDpt096Tmb+mnwvDMiD5tctr0NN89ofnQCnf3p8KK2U375Jaxv2hsdqe8Aq4cSF3QW+una5cqhBBCnIvFBQw3GMMwSCaTGMaGvxRrQhHU4QS4plPx3SFNxFTszK78WL6GXFuwDsA0l06YMpko5XKT3OwazFjcQimIREwy2SiRiIlSkMnEKJcaXHvdILd8cDu//P+7hhd+OjS7DjU43/Bs96lIrDVLmAnDmULpJ2NwY2+rQ9W5GK5q7LOsIajZQZF9DTz9Vv4srWLFqWRcECBxIFokFlbXhp85DYVCdHV1rfVpbDje7H3wsGUQnd3pXnGhPwk39wabo2quJn6W3vIn01rT3ZMiZBm4hsIw1ILuRnM77u2mRzodFIU/cTzPtu2djI+XqVZsEskw3d1JDh+a4sl/PkKuLcbmLVm+/rXXuf6GTSgFjbpLR2eCnr5W96k5PxrVXNe19DkPVYKksi/xzga3Y6WgrNT2M3SdunIwzdvjVcp1B/A5Nl3nEmkfumwyLgiQOBAtEgura8P/CqC1xnGcC7bTWpyZ7Wns2ZnTgwXN+2fLR/1kDPbng/WZhabGPk0LzVNF4xZ33HUpWkEyFcGabcE5t+P+Zy8Oc9e9u0ilw/QPpHn5pVGqlSZdXQnSqQgToyX+218/wze/vh/b9th7dT9PP3mEiYkK33zkTTzXp60jTjwRIhy3FiSmtq+ZqEPvKS09665mqKJ5fkJzy8A7vwW+JQ0zzcVfr7qashNcp2TE4oqB9PySW9uV+F4JGRcESByIFomF1bXhZ05t22Z4eJj+/n4ikXdfsfB3q75shOF8A1drCjWHrnSEmguHS5ruGBzIazpjcLwM3XHFng5mu0adhYJazeYjH72C4RNFXNcnk42STkeoVm3uuPNSanWbUqmJY3s8+NAVPPfMcbp7U1iWSUdXivdev4lEIkwsHuLZHwd1TiFIcE3T4PDbM/T1p8nmYoQjBnNd6p8e0Vx/Sm1WX2u+eUQTMuGOQbVg09K56ogFXaFO7aD17JhmMAkkIBVSNBxv/hl9WYntlZBxQYDEgWiRWFhdGz45FWsjGjLnd7j7s7+J5qJBfc6+hCYbgWu61LJ2tCulMFSw5tTXYJoGhUKDl14YxjCD2/vxeAgNTI5XCIctbrplK6MjFTLZGD19abSv+cbDb/Dgx67gyr09PPLVN5iYaJWMikYt7rxnJz955hhX7unFsV3+39//jLvv20VPX4KhalCj9dQKA28VNHUPbuhVJEPnZ+NQ2FAkQ5qZpqZtdu3qVN1nqg75Btg+XNEGx6eDzWEKyMal5Z4QQoh3B0lOxZooN9z5W85zfdcHEkHJJAUkQmcvtWQAzXpQBD8/UyfXFqO7J0V3T4rHvn2QD9y8lce+uW++TBRALGbxkZ+/gscfO8D7b9xCPl8nnY7w42eOEo6YNBouxUKdm27ZSiHfmN/BH5vtFlUuNUimwmitmZys8OUvvcKv/cvreD1vcGv/4vMdq8L7emDwDKWlzkV3XDFZh7ZIsPThB8Owuw0uzylMQ1G3PabLTRQ+mZh8mwshhHj3kJ9aYk3MbYjSQN3xiIVNtmUUr0xpchHIROBAISjMvxQDmJ6o8/ijwdrQOeGwyZ337+YDH9jET54+wofuvpRa1aFYbJDLRdmytY2nnzxMqRiUlJrJ14hELDxXc+fdO3nm6aMA3HTzVg4fmsJxfN7aP0Gp1CQWD3HXvbsJRwx++M+HQUO95jB0osjtuzvmW3rOKduaqSbnZZ3pqRxfzy8R+PGY5uZ+taCb1uHJKgpNPGywdzB1usMIIYQQ644kp2JNxMMmlqlwPZ/Xh0ps60owkItxY6/m1emg09FJE56LNOsejz+2H9vxFiy9bNoe3/7O29x521Zu+rltTE1WKRbq9A9kME3FIw+/QanY4EN376RQqJNIhIlETN73gc288NyJ+fWlzz17nPseuIzJqSozU1WSqQi9fWlqdZsnvnOQqckqGtAa8tM1VFBgYF7V1TwxpLlzUHHq2tDzYbgCbVFNpBx0hDq1zet0xcYgWJeq3oXtQ4UQQmxcGz45jUQibNu2ba1PY8MJWwZXD6Z4/miJoYqmrHwGcrAlbVD3NK9MaW7qW/q1plKcGClTKTdxXY1lKkzLwHF8tIJcm8HxowVSqQjHj+XZvqODcrHB8ESVK/f0EouHeOmFIa65boBsNkaj4dDdneKa9w7Q1h4nlQpu43/tK6/RqDt0dSeJxkMMDGb50hdeBkCp2ZRTQVtHnJ9NaCqOz+VtinQYvndcc32PIhVuJYbV2R3ziRWUxzodDbynU/HEkObmvsXHS0Ytag2Hmu3j+loK5a+QjAsCJA5Ei8TC6trwyalYO/GwiSLo7FRyWsnT7pziSFHTtsSGSAMo5ZscOTxDqdQEDZ7no5QinYlSrzlMjJW5/IoufvbCMNdeP8hT/3yIWt0hFgvx1n4b2/a4695ddHYmQGlSqQjRmEF3T4J0JsrXv/I6k5PV+ff0tebjn7yKJ3/w9qLzicVCDA5miCTgcFHx+rRmqhEsS8iFg5nLhqt5blzj+DCYOrcGAyc7WtKEDMg3IW5Bcom9Ttu7ErxyvIiBZqLUpC8bfWdvKoQQQqySDZ+c2rbN5OQknZ2dhMPnv/+4OL2646OB9igMOaDRqNlb4Hs7FU+OaO7cxPzXILid/8R3D7JpS24+MdWA9jXFYoNsJkqp1KCtLU6tZvP0Dw9zy+2X4Pua6ckqyVSYru4U8biF6/qAIpEM8e1vHeB9H9hEOh3mE790FaPDJWama7R3JujvT4OCE8eKC84/Fgvx0Cf3Ek9YeMC2jGJbRtFwffbnFS9NaqYaPvkmdMbgAz2KzDvoDAVBovzipOaWfsVPxjS2B64P1illtjKxEImIRb3pULelO9RKybggQOJAtEgsrK4Nn5xqrWk2m1JYdw1MVWwAIqYiFY4EZaBmc7f+hKLuBrfHbxsMbuUrpYKd+fk6l1/ZQzhsUqu1Ei+tNZ7nE4+FqNcc7n3gMv7xH17hn774ColEiC1b2tA6Tq4txnceP8zll/fw0+eOc+/9u5kYr/D0Dw+z+/JuwmGLXFuMzq4k1apNqdygrz/Nr//WdQyfKDI9XaO9PU7/QIZ40sI75XNFLYOrOgEUXzno85Gt7zwpnfP6DPTEoeJAyYYHt6nT3rKvNt3ZdadipWRcECBxIFokFlbXhk9OxdqYqticmGnM//3KdsWTw5pbB1ozpZdkFKaC757Q3L0pqGWanwlqd77xxhh33rOTx761f0GpKMsyueOuS3nxhSGuubafX/2Nazl4YIrp6RqZTJR4PMQ/f/9trn3vIG+8PoZtB6WoEskwMzN1nvzB22SyceKxMPW6Q7Vic817++kdSBNJWFxyWSc7jaDkle/7ixLTk52oaDrj5y8xHatpXp7SfGy74iuHNAYQsxQlW5MOL36PWNikaWtO5Bv0ZMIkIvLtLoQQYv2Tn1Zi1R2brnNkKkgyNQaZWIidOYOqq3n8uObyHGxKBcnW1rRiX17z0qTm8jbItcWAIEl97dVR7jypVFQmE2Xzlhwv/2yY/EwdDVQjUQa35EimwsxM1XBsj/det4k3Xh8LEl0F5XKTWCxEpRL0BK1WbGoVZ/58s7lYq2GA7+Mv4y65pzXPj2vu2XKmxHRxl6czOV7W5CIKRxv0xHwSIc0PhnyiluKGnsXPv3IgzSvHiziuy2ixySVd8u0uhBBi/ZOfVmJVzVQdjkzV0RiYpkE6arG7L6jD+Z5OxVgNfjKmaXqwY7bG6R2DioMF+Oohn/v7UqiQgXZ8yqUmjz96gEQqTCIe5viJAtlcjOGhEl3dSTq7kgyPFnCqTV56/gSpdJRGw6U6u5wAAA2dnUmGjheXONugbmp3T2rFt3IOF+HydkXUXDr5PFQMOkrtyC7veJ6GLtOnw6tw4Gd1+jMx4vEkfsJie3rpc4uGTLozUYamqxRqZ6jLJYQQQqwjGz45tSyLzs5OLGvDX4oLzvM1rw2VgWDO8IbtuUU1OHviivu3BLfycxHoiClChuKyNhhIwqGSwU0f2sWj39yP4fsk0lFKxQaNhsed9+zk5VdH6epOcu11g3ztn15jquJy1+3bmJ6uMzZaIZ2JYp20eygcNunpS3FTahvffnTfgiQ0HDa5695dRGImK91SVLKDjV6n05eAbxzRDKY4bQI7xwBOjNT4wbf3Y3g++abGUBANm/z8g7sgHQeg6WlKNrRFgzW6AKmohUZRaXoU6858Ny5xZjIuCJA4EC0SC6trw19l0zRJpaSDzmoo1JxgZz0Gu/tSKKU4WtKkI8z3iAewDMX7euCJE5qHLoG5W9/psOKqDjCI8y9/dS+jo2WKhTq5TIxMNsr4RIWdewfY3Jfky//0GtWGi1Kw7/VR7rpnJ48/doBSsUGuLYZSakHyGYnFeOiTexkfK1PI18nmglao55KYApQd2HSGsIpZiht64Z+HNPdsbn3GpdTrHt/85n4SykcTJKamAtf1+NLD+7nvY3t4oxIk3J0xRTbS2ljWngyTjoUo15u8NVblvVuz5/BpNh4ZFwRIHIgWiYXVteGTU8/zqFQqJJNJTNNc69O5qIXM1ozlyTN4x0rQ1rnwuW0RxRVt8IMhuGVALygn5QPhmMmW7TkMlZtfD5rrjKGBw2/naTZdfA2ZMORnGrz5xjh337uLSrmJ5/t09aTo7k4uSD7NiKKjN8rgtiyg0FqfU2IKUHGC9aFnsimpqNrw1Ajc1LfwM85RSnFiuIzhe2AqfB3c4vc0+BpSysMsVbh3WxZfL32MtmSYUt2havuMFZv0ZJYoICsWkHFBgMSBaJFYWF3G2Z9ycXNdl+npaVxX1uRdaPFI6xu64QZp32AKjpY1SilMY2Grzd1tCtOAkeqiQwGzpaN8jdZ6/s+KYLNUw4N0uLXzPz9T50dPHeHgwSlyuRhbtucInTIr6vsexWIBx3HeUbmQphesmS07Zz/G7jZFbxx+MgosUfapZGvGJuutz0zQMhWCLlUND46N13l2HB49qnn0qM+rU5p9M0Hh/zdnNINtMbozUUAxffJ6W3FaMi4IkDgQLRILq2vDz5yK1WMZQaro+JqvvmWztd1kc0rxnrTHicMl8jN1cm0Lb6fvaYdHj2n2dsAVbXP724Mk1lDB7OHJiaSvIZqOETKCx0/N96oVm1DYxPMvXK26iAl3bVYklvHdVXE0EUOjFHzjqCIegqqtcX1N3Q2WMmzOxqg5UHeDTVQn5fiEDNjcHWOwW6G1gac1Q5XgOuSiwfpTAMcL0vAL+LGFEEKI80KSU7GqEhET3fTYk7HpzcYxSjW+8c39VBselgGpEETCFnfdu4v2rhjZiOIXL4WvHwpuk1cdxdZIUJv01GQWYLrk0J2LElY+dtMnFDIXzMbO7b5XSmGZar5e6fm1vMTU15pnRjW9ccXOuEeuVGFytE5fW4zuniTT2sQyoD+U5OmwieN4hE1IhRT5piYRglh0YTUBUyk2L7Esqlh3Ab1g9loIIYRYjyQ5Favqkq44L58oU2u4RJ0m33n8ABHtE4koGp4m34SI6/LtR/dx30N7GfcMdmQUN/bB/jwM+jW+9LX9+E4roQyHTe7/yGXYjs8/fHUf2XSYn7tlG9977ACVsj2/Qz8cNrn73l0oBW+/Ocn0VJX2jgT9gxniCYvzd7NGU7YhFYaTNzo9csTnw1tbK2lemYIr2g3i1RqP/NN+pqsebVE4fNJmrVEzytNFk5//yC7++fH9TFY8mp4mFYamMrntjl2UlEn7Wc4obCoaHlSbZ2obIIQQQqy9DZ+cGoZBLBbDMDb88ttVkY2H6E6HKdY9JkbL5CtNYiETQwU1QaMmNH2YqHhMjJU5EslgKZ+taYOw6/Glf9hP0/ZIhcGYTfyatsv4eIWnf3SMCB4TU3VeeW2M2+7ZRaPapFJqsGVrGz19KaoVm7/7789Rb7SK7EejIT7+C3vp6osTDkdQauWxUGj6DFcVR0oaxw/WuyZDiuu7W8/xNdRcTdwKzntvB9RrHv/fw/vxHI/2qAKtsW2PWtXhmw+/wYOfvIphTNq743z8k3sZGS1zbLxOd3uMbYMpni8aKFsHrz2D7kyUo5Me+arDWLFJZyqMeZq2p0LGBRGQOBBzJBZW14a/yqFQiN7eXkIhqf+4Wnb3JhnIRcnn6/haY8+uh9RaY9suXsMhgUc5XycbMXhtGp4Z08xMlPFcD6WCOqKe1jQ8jRcKUyo1ceo23mzTpXKhznM/Oszr+ycZnWngmQaeD1/4+59Rqzv4s7vdPR+qNYcvfvEVKmWPXC634p2YT49qXpmCiKG5b4vBg9sMbupT7M9r3ioE60cBrulUPDPSWvRp+4ojQ2Ui2iMVUniuz8xMnWKhTqXSZGK8wuEDE1weblKyIRQzMbuy6M29vGJkGHENhsuanvjZz7EvG8UwFBrF/rEqzx8tyuaoM5BxQYDEgWiRWFhdG37mdG6nt1JqUUF4ceH05yIUcjHm9tO7rk+p2MA/aXOTqWBQ1fHjEWqeYmyyjuu3yigVbYia0J0NUSrUcV2NpxQGmrmGSDFt49RsCtM1wgoaszOmimC3+9xdd6fhMD5c5JLdHfjaZyVtRW/qhVN/zwsbig8NwmQdHjumiZqaYhPiIXhhQtMRhWMVRbpcx1RB2apioY7r+q09XBqmp2oMDZX4wO07IGrSFdP8aFRzQ4/iyeHZYvzL+C42DcWewQwnZurkKzYNx2ffaJVrtpjEQrIO9VQyLgiQOBAtEgura8PPnNq2zdGjR7FtmUVaTQ3HI9sRJxw2CRlGKzHVoH1NKGwSi4X47jff5Jq0z829kM7FMFRrA37YhLilaNRdstkYhqlmi/xDwgr+a7hQ96CtLcbkRAWlmP/vVNPTNSrl0llLhTi+xtPBjOhwVfPyZNBa9fsnfJ4Y8tmX14xWNa9Oa46WNQ0XGj5sSiuu71Y4fpC0fqAXOttjoMCxvUWJKUAmE2VkqMj0WBnHD9aOXtetOFjQeDroQrXcCdBU1OKyvhR7N2cwTBPX1/z0SJF8zTn7izcYGRcESByIFomF1bXhZ07F2ohaBuO1Kvfct4vvf2v/fGLqeT6RqMWH7ryUnzxzlOmpGsNDRfovaSPbmSQcNrHrwaYexwPb0FCxSWcixGMhnLJN3FK4nomtwVAQj5rE25I0Gy6eP5ucsjhBbW+PYzsO4ejSfUcn65qXpzS2Bx7Bb3a9cYWhNBETru8GRyvemNYMleH9vYpUKHgT19dM1OHFyWBWdyCpmGlocl0plGXiuDanllaNRi3iiRClUoOZyQqbd+RQCrZnFcmQZqzmEzKg0JzbfHV2tq+JhU2u6E/x8vEihvYpN1xycblVJYQQYn2Q5FSsCaUUIVNRt+CeB3YzOlRierpGJhMlFg/x02ePMzlRBQVHjxaIdmVxLJOPPLCLhx/ZT6XhkYlAxYFoSBEOW9xw01a+/c19FKsuhqHIZqOEYiFu+tBOvLDJwGCGWCxEve6AWnjjPhYL0T+QoVCdIrnE+b48qTlW0XygR9ERC145Wde8MKlJWIo7BiFiBoVVXR9u26TmN2xBUOO1LwF9CYWP5mgR3ipAzDK5855dPPbwa5RLzfnnR2MWd969k58+exxfQzodIeRpThwvzJfQel8uydsNkxcmNCEzWO4wWg2uScXRWAo644qICWEDXD9oePC+bkXcap1dIiy39YUQQqwfkpyKNZOJWYwWbXwb3nxjDN+Ht9+eCpK02RadaEiloxQmK6R60vT0xLnjo3soT1VQ9TrxVAzD93jhhRPUXbjnvqBFaaHQIJ2LsmNXF2bMYn9ec8iz+MQv7uXLX3yFet3B82dnVuMhHvrkXiIJBUt0o5qqa0oOfHjrSYtUZ32wX80mpUHd0ieGNN3xhYnpqQwU2zKwLRP83VRxPvGLV7N/3wSFfH1Rgh6JWvT0pHjkq2+Qzwfdohxf4xomt9+zi2IqxkQ9WKPbEYUr24N6p0+OaI6WNFvTiqQVrM+9qVeRCiuOTzdRaOJhg7aEzJoKIYRYPyQ5FWtmsC3KiZkG7V0pqjWHYqHRelAFyVYkYhFPRhifqHL5pRl8DVUzhNGdxfFzGOUiTzx+EENB3YUfPXWERDJMLBZicrJCPBNnKp1hZ0aTjsBwJcEv/cZ1TI4WyU/XaGuPMzCQIZ60aLpLr73siCluji3+emdsYQJ6sBiUkNrTvrLF8p7WRGMmHR0J3j44FSToxSaeDm7t33PvLkrl5nxiCkG5LcP3ePzR/dz983uIRk36Ewvf97aBYCZ3rqvWyfJVB9B0p6OyuF8IIcS6suGT03A4zObNm6V22RowlMIyFBP1Jvc9cBlf+cdXaTZbm5Ei0eDW9kuvjXH1e/qZKnlUJ0vUJ+ukszG6e9McOVTF9SFmBTOtHppqxaY6u0vIqdTp683y3ROaZEiTCGlU2sTt7iDVCzty4PsaD7Asi87OLoxzrP85Xdf0JVb6Ws3BIpRtg950lKuuHaBcalIqNEhno+QyUTK5KI9+7zA+en5GNmaCDzRsDzdf4cVqhqbrsy1zahyfmpYG5qoihExJTJci44IAiQPRIrGwujZ8cqqUWnFdS3H+uL5mpmLT15Ph3vt3Uyo2KBYbZDJREskwr7w0QrXUoLc9xte/9DK+51P3NFpDWy7GNdf0Bbf/CXbv216QuM3J5mIMphW2pzhc0mxOKibrmrrn4/maI4VgY3zcVFzfAzHr3Aee0RoLiu6fSb6pGauCo6HQDHb+18wI23ssvFCNeDpCLhmivTPB62/lmZyuYxmQnd34ZCpFxdaEDJjJ17nnuizOCpo/RUMm1QbY7vlu3XpxkHFBgMSBaJFYWF0bPjl1HIfp6Wna29uluO4a8LVGY9DEJ5uL8doro5iWwchwkRPHi4TDJh//hb088rU3GBspkWuLEbJM6q6mVKzj+5p01KRqeyhYsOM9HDbp6knha5/OKNQdxYlK0MFJE5Ri2pRSXNEG+SaE8CgUyqRS6RUPQoWmT9RkmV2XNEdL0B4DS0FPXGEZMFnTPDllcHNvmsG0wtdwoKCJxyzioWDZwsKjBAl5Wy5GyFBYK5gETURMpsuKqYrD5nYtt/ZPIeOCAIkD0SKxsLo2fHLq+z61Wo1cLrfWp7KhTZZt9gykufv+XYyOlDl0cJodl3aSzkTIF+oMDxVBwcxMnc7OBE0vKD7/05dGuePOHXzz2wdxHQ9TzSaepsnNd+zkqWmDmhO094xbELUUxZrm8jbFjmwrIctFwHU1zWaTZNIHlp+cTtY1PxqB/qW2+c/SaCZqQeMATwfbqqbqs4X5LXi7FGxYen+Poi8Jnq/xfM3rU5p7B1JkYxa1Uis7dXwd3K63TPr7UuhT61Cdha9Bo6jaHk3XJyqF+BeQcUGAxIFokVhYXRs+ORVrKxsPMVN10QRrKCOJEAoYGS5y8KBNIh6mpzc1/3ztB73nI6YFWjMxXSdfanL3x/Ziz5QZnazT0R6jrStFZ8Zkq4JTd9j7Gh4/HmyQ6o6d+4xhw9N8/bCmKwa3b2rVNJ0zXA1qok7WYaii6U0o2qNBMtoeDZJDhSZuQSYMl2QUm1LBMWYaQSeo67oVsZjBHffs5Ktf3z97lYLPEI2a3HH3LkLxlSeWx6drGGjiIUMSUyGEEOuKJKdiTSXCJjNVh0LVJl+16cpEGRstMTZWnn9ONrtwq7zr+oTDQZqmNfimxf6qwYd25DC6s7yV1/THl+4CBcFGrJt64dkxTTQEu7KLd94vx2vTsHW269PpVN1gRnVvR6vk1MlsDw4V4eY+SIdbj4/W4PoeRXcsqIu634tx30N70MUKhXwdKxmjuydFUZn8eERz68Dyz3+s2JhN1zXp2DKr9wshhBCrRJJTsaYG2qIMFxr42ufgeJX2VJT2jsT84+VSk3giRCRqBTv5FYRmC8i7GtJxk2hbkvF80Pd4awoSpuaVabix9/QJWyqsuH0QCja8PAkDSc3WM9yWX0yTCsH2zOmf0Z9Q9CcWfm2irinZwVrbsg3TDbilXxE+JXG9vK3196eGYbKhmbENPrwth1/OsW/GZ1dakfI1w5WgWNRyHZ6sofBRwI7u+LJfJ4QQQqyGDV8TwTRN2tvbZRfeGolYBtdvywLQdDyqDYf+wQyxaGvB+fPPneCue3YSiVgYShEKBxuiElGTn//wLt6omqRPmgDsihsMVc7+3kopchHFrQOKbWkwDINUKr3MUiGKXTlFaIVlp0wFDVfzylTQAOD2wcWJ6WRd870TPo8c8Xn4kM9EXbMjAz0xRb7h89yYxx2b5s4imH1diWgo+HydqTCGbIRakowLAiQORIvEwura8DOnlmWRyZxh+ktccJHZmVANlBoufekID/3CbCenhsPkZJWfPnuce+7dRShiki81SWZibB1IoSMmjaJP85SKSMkVbqYMdtmbxOMXdiaxPapIhmAgCdnI3M11TakJx8pwsBhsbLp9QJEMwyOHg7JZkw24phO+d0Lz/t5WB6qGD+WlewfMc32N7UN8djt/yAyS07kkVSwm44IAiQPRIrGwujZ8cup5HvV6nVgsJr8RrQHH89k/WmVur3nT8fCAnr4Ev/5b1zF8osj0dI329jj9AxnCyRCHhjTXDAbrTQ8WNAPJYNPRHF9rmsuYTbT9oP98zYVkSOH7PrZtEw6HL1ih5aqreSsPR0uaph98asuAsAFXtit+fnurRepTI5q2SLC2tupCxISKw4JOUAlLYfua12eC5546D1qyNW8V4YbuoFoBQH12qvUcew1sCDIuCJA4EC0SC6trwyenrusyMTFBf3+/BNwqqzsePztewnaDWqddmSj9uWDzkwdEEhaXXNbJTgM8PyjlcaAQ1CzVWqHRPD+hyUXhspPWaL4+s/Dvp/O1Q5qYCVd3BrOZvu9RLBZob28/5+R0pqF5c0aDUtzYC3PpYrGpOTo7M/reLsWedoVpgOuDaSzdx+nGXnhyWLEzBy9MaF6YhGu6Fj8vEwKtNTNNtWidzlQdbupV87O00NooJrVNT0/GBQESB6JFYmF1bfjkVKyuhuMxVmxSbnjM1By0Vpimxa7eJLnE4p3jvu/jz9+yDxK/m3qDRQD/PKTxdTCTeOlJNUun65rL2uBMm4Qm68GGpnu2qNM0+Fy5uqv57gnNTX0w04DHjmluG4D9eXh9RrO3Q3H/loW79s/UkGpfHnw0kzXFjgzsz8999oVsHzpiit74Up9j8dfiYZOm7eB6K6uNKoQQQqwGSU7FqnpzpEKp4QEKjUE6FmJHT5J4+Gy/iWpenYbBJIzV4ckRTTIEl+VgT7uaf86PRoMZyqs6l044pxuaZ8c0SsEHeoPEdLyueX1asy2hOdcVpzMNzWPHNB/ohf6EQX8imKH84TDsyBl86tKVJYLHy5qxmub9PYpnRjW39AeF+pdKNtuj0HQXffm0YmGTPIqGu8KdVEIIIcQqkORUrJp8zaHU8NAYDLTF6EpHSEQWh6Dra8br0PSYL1D/2rSm0AxSM8PQbErCgQLcMdi6bb6/EMxYvqdTkQjBaFUz3QxmKj2tmalrphqaa7sUfbPrNsfrmmdGNNf3KNIG1OyVfirNI0eCHvc39So2p4PjGsDOmE+2WCb/Vp2htqAuaSRmcmo3+6anOVCApqtp+IqKozGATUl4/Jjmxj7FVANi5tIJbsMLZoK3pJc3AzxZtgGNKbf1hRBCrEMbPjlVShEOh2X93Soo1lrbyvtyMSIn3dP2teZ7JzRT9eBW96VZqLqKuhvUBW160J8IeslP1oO1mj+/XWEoxeszmpmGJmIqrumEJ4c1JVuxM+4TmylTLNTJ5mLs6U6R7jXx0ZRszcGi5kQZ7t6siFkK1zWwLYu52Unb01gm8zvjl6a4pQ8yJ63pNIDpiTqPP7of+6Q6T+GwyV337qK9K0bB1rw+DccrmroL13UHiW3cCjpH/WBIEzIUd22GQpOgbmufiVIsaFU6XtMoHWyaevyYz94O6E2cfq1ApeliOx4K6EpLAf7TkXFBgMSBaJFYWF0bPjkNh8MMDAys9Wlc9GaqNkenG4AiZBmzpZta3pgJ2oDeuenUxErR9IK1pfvzwc76Dw4Eu+shaAv6dkHz4W2ttaPv7YZYrc4TDx9YlBx+6N6dDBlxpho+6TDcuQlisyWWLMuivb1j/vlfP6L5yDZF+Cx7o05OTAGadW9RYgpg2x6PPbqPD35kD8/lDfa0K27oXbjmte5qnhjSfKBX0R4NNjhNFF12uxWOvFInd9IM7HDV5808vL8nSK6n6z5jNeg9pfD/yQ6OVVBoQqaibYk1viIg44IAiQPRIrGwujZ8cipWR7kRJGo+iqs3Z7Fmk1PHD9Z7Hi7Bg9sW/0bqo3l1KlhH2h0PdsDPFY5/fUZzIK+5Z/PJCZ5iMOzx+S/tx3c9kqFgJ7yPZrLi8k8P7+eTv7CXvR2nX+Pqo3niuCZmcdbE9FRKKcbHyjRtd748lgE4fnD73bM9GjMVHrwkt2AGVKP52aTmWAk+OKDIRBSmUkxN1Hjsa/uwfG++WH84ZHLnvTt5vhbjipyeT67bYwbtMU7L9XwqDReFZkf3GTJYIYQQYg1t+OS02WwyMjJCX18fkUhkrU/nolWqu4CiMxVZcDtfE2wc6oqzZLei16eC294fuyRIQKuu5khJM1UPvn7/VkX4pFlYpRSHTpQJ49EA6i4oFTw3bkEUn+p0mbbMwuQQwHZsfnZ0hrfdHNf1htmRXfntm5INQxN1inawOMCf/ZAhA6Jm8P9qsc6RUo5DRR/XDxLXpheUy7p7syITVjTqHpWKw1f/6TVMz8cwDRq2h2GA9jVf/vp+Hvz4XhorKHnl+q3Pm4uvsEvBBiPjggCJA9EisbC6NnxyCixKUsT5V2q4aBS+1vhomm5wO73mwKEi3Ny3OBH86bjmaAlu6FXkG7Avr5moay5vC1qHdscX1wetOFDK17EUeDpI/MIGZCJgzj63kK+zWeU4tZLS08Mau6n52KWKSHj5iekzoxrXDwrsR03Fto4Y2dO83tUaEjFKzeBzWCrYhd904aY+sOZmS793mMt3tFHM1/E8H6UUmUyUWtXB920S6SjV6TLlbJZNyzzPqcqKd3ttaDIuCJA4EC0SC6tHklOxKuZ2hmfiIUYq8LMpjdaaiBkkpp2x1q77H41qyk5QIunjOwx+MKSpucFu9BtOat15sqqrebsAIzXNZV0xXnKDpNQygtvpJ88vZnMxqo7mtSnNaC3oJlVzIKQg4sB3jmsSEZ+6B52xIKFui0BXXJGLLH7vD/QGSbGhFDEL3HqKcNhctObU1RrPNNk+mCIWh4OFIOHe26HYnAJQ1Ose//TwfrraopQKdTw32NuvtaZUbJDJRClXmlRKDQozdSJt2WVd/1Ld4chEFYUmHTXnW5gKIYQQ640kp+KCczyfpusDBkcnaySjNpcnLLZ0xBfdys9E4OZ+hfbhxUnNNw77dMTg1oFTkynNvjwcKWkMFXRC2tuhuHMTTBVSWJZJBC9Yb6o1+Sakw5poxCTaluJrh33e26m4ulNhGMGOfNc1ODGm6OlUxCIGFUczWYeiDWUbYhbklrybE3SYmhOJBbvyT90UFY9Y3HXvLhLxoJxUMgT3bVEYKtiRf6ICTJYxfY9S1WXrpoV9nH2t8Twf01B4viaZDDPeXN6/QX62UoKsNxVCCLHeSXIqLjjH0/Obg7TWVOpNynWHfNXhmi3ZU56tMJTmSDWoa7o1HSSdJ/PRPDUc3Eq/ukPRGWN+g1Whqflp0eChB3fxnUeD3fpRM1iraiuTD96xixlt8slL9JJrXOMhCM1uPEqG5pLO5d7i14DCB9q7Ynzsk3sYHytTyAelrE6tc5qNwIsTQTmpjqjikqxiulpHaXAqdRLxMJGIRfOkCvuep1GGIhI2yWWjDPnwypRedI1O1nA8RvINFD49mTCpqHzbCyGEWL82/E+pUCjEwMAAlrXhL8UFE7GMoJ+79rEMRTJiUai71JouI4UGfdno/HP35TUHC5qdOcU9W1jQ6nPOD4Y07RG1oAtU09O8PBXMpH5oUNERjS9IDmPpGLH2JEOOyfbo0ompaQalpFbSN/mtgubVaY03uxt/Vw6u7w6qEoRiJoPbcmxWOXwdJOY+wc78twpBY4G97cHs7Vxy3UhGKObrmAa8/LNh7rxnJ9957MB8gmqaCtM0ueOuS/G1z6ZEUAf2sWM+g0k1W50gSHyzs0sQRgoNPC/Y7b+t81x7YG0sMi4IkDgQLRILq2vDX2XDMAiHpd7jhWQaivduydB0fTIxC0MpDoxVGS02OTJRJROz5jtF7c7B7tzp10M+N64xUae0J9X8w1uaXTnFx3cEa1J9WJQc+tonacM/D2k+vG3xZiql1IoHnh1ZuDQbnO94TfPMqOb7NlzZDulwsBSgtfFKc6ISJNeXtynunS3+D1C2NaWKG6zDjZg0Gy4z+QbP/uQYH7r7UmpVh3K5wabNOQDeOjDJ7XfuoGt2re4eH45VguUHTR9Ga3BDz+xM7uwu/Y5UWNaaLpOMCwIkDkSLxMLq2vDJqeu65PN5crmc/EZ0AcXDJvFwa0ZyZ0+ChuORr3m8OVLmiv40sbDJmW6hj9WCMlIfv2Thc346rrm8LeiydCqt9XxyqFCkw5CwNC+Ma/Z2BrfV26KwM6vwfY9KpUIymcQwljd7enKCmw7DfVsVFQcOFYLarJ4OWq0mw4pDBU1PQnHvlqDAPgRLFF6bgsMl2OVUOPjaKB+6eyfffvQA9XqwTvTRb+yjvT3Onffs5M1940yNV7nr3l0LlgiYhmJbeulzrNoeILtMV0LGBQESB6JFYmF1bfgr7Hke5XKZdDotAbfKdnQnePl4iabtcniyyuX9p8mugP15zbGy5sNb1YJb8nVXc6QEn9ixvBnBo6WgEoDtwzeP6Nlb4LOJou9Tr9eJx+PLTk5PNjcLmm9ohqpweZuiLaLRKJSCzUm1qJuUAUzUNbtyBs7hOodG69j+GPfev4tiqUmj2qSnN006E2Vyosxll3XTdWtyQWJ6NnPvOFNxaLR5REMr/2wbjYwLAiQORIvEwuqSKyzWTDxsckl3nDdHqkxXbGzXJ2wtTDLHZm+Vxyy4Y1AROqXt6VMjmht6lrdhqdAMjvWR7YojxaAl6vasInYecrW5Na+HSxrHh9sGFP0Jxdk2U71dDJLay9oUb03FiFtQLTT49hOH6cyESSXCjI6Wqdia++7aTld/Gt/3l52YAmzrSvCzow4N1+fwZJ3L+pLv6LMKIYQQF5Ikp2JNZWMhIpai6WpGCg22dMSZqmsOleCtvCYXhd05xe62xWtEAUaqcOemsyenjq95+LDmgwOKhKW4rA2+cVhzrKJxPGj6mt1pTcaBtiUKLftao9TS5wDBbfn2CEyG4Iq2ucT0zA4Vg9ngW/sVx8o+8fYkqZhJremhNTRrDs3ZElAVbZDOxvD9laSlActQs3UEpIi0EEKI9U92R4g1FbaM2d36mnLD5VhZ82YetqTB1UEd0Mva1GmTwuXRPHZMc3OfYnMqOI6hFDtyQUL50e0GH9uuCBvwdjFIYp8ZDQr/Q9D289FjmtIZaoruzikiVpCYbkmf+VynG5ovHPA5UAgS06NleHlS05kN6qA6yiR20q+N4bDJA/cHa0yXq1h3ODpV48WjBZ4/nGduDndbl+zWF0IIsb5t+JlT0zTJZrMrKh8kzq/2ZIgjU3UKVZuwWeHG3iSGUtzaD2e7LZ4MBbfUlyo5BeD5mu8PadCwPbPwOZe3GTw3rnltWnNlu2JXu8mmWJJozOJ4Fb5/QuNpTd2FVBgmG5CO6NMmyoPJ5SXQ7VH45I6gfFTZ1rw4oXlga5Acd3TF+Y1f2buoPuqPZgz6fY1pnP09pio2+4ZLqNlNUIpgbe1lfUlist50WWRcECBxIFokFlbXhk9OLcuira1trU9jQ0tGLGIhg7qjmSg1KdQc9gxm6IsrxopNinWX3kyEdCyE5wcdoZRSeFpjeyeXalqsNtugaVdu6cev71Z897hPfxLaIibJZAqAbWnYllY4vqbY1NQ9xatTmoMF2JKCwVRQpP/cKCwDKo7mByOaW/rVfHK9VAksrTUN18c0lnej48hkFYWPAnKJEJmYxUAuuqzEVgRkXBAgcSBaJBZW14ZPTn3fp9lsEolEMJb5w1+cf1cOpHhzpEKl6WG78MKR/PxjCs1EsYFpGrieTyRkkowYPDNucHlnCEOHqNsQCRmLiuunQkESuyN7+sSsPxmUecp1geM4WJaFYQQ3wkOGomO2luhAEsZrULKh6rKgZelKzBXh35/X3NK3eAc/LCyBBZqx2vKPH9Q1VWg0tutTsz1OzNTZ1B5bsvmAWEzGBQESB6JFYmF1bfgr7DgOo6OjOI6z1qeyocXDJtduyXBZXwLL8Gdn/nxiobnb0j6+52Lg4zgO+UqTXfE6fq3C84fzvHAkz/OH80seu+rAmSYNexMwVoefjTs8e3iarx50qCwRDgpFT1xxaVbRHTv3JM/1YboOd29eOjFd6p3bI0FSuxw7e1PEoyE0BpWmz3jJ4eh0g5eOlWg43jmf90Yi44IAiQPRIrGwujb8zKlYX7pSETqSYXwNtuvPF+6v2x625xM2DcoNF9fX5KsO5YZHww12sNsuHJqosrUzPj9DqNF0x89c0qktomh6mpgJRgj8EPxgWAOapAW3Dpy9JNSpXp7UFOygCP913YpUiPljhAzF+3uXdxzbC2ZNmz5oDcuZ+MzGQ7xnc5ZK02W00KDccKk2XCpNj5mqQ19W1kwJIYRYvyQ5FeuOoRSGAuukjlKxsEkMc/7PAH3ZKFprpqsOhyZq1B2fkXydhuOxuy+FoYJd/jf3nf09e+PBpqe2pOKKdgNrtt7qRP1cSi9pBlOw3YQjJcVTw5qGBxaaOzdD1DKYLc1P2dbUPCg0wfGD9qMAEUNTtIMZ3S0pRTbMSvNjkhGLHd1JPF/zzMFpFMi6UyGEEOueJKfiXU0pRUcyTHsixGvDFWaqDjMVm+PTdbZ0LL9sUm9CcWBGc0Vs4de7zun2vaI9GvxpTzvsaVccLml+Nql5/DgkQj62B1ErSHxDhiJsQlcUsimoO/DsOFyShge3KqKW4s0ZODCj2d228vNpON58XpuKyre8EEKI9U1+UoGUhrgIKKW4sj/J2xM1hgtNRgoN+rLRRR2nTmdbWvHciILYhVmGHTHhwW3qrBuSXF/z2GTQpjUdbj13Wwa+8jZsz2jCpymbdTrlhts6j2VeDyHjgghIHIg5EgurZ8P/pIpEImzevJlIJLLWpyLeIaUU2zrjREMGvudxZGoFW9yBjrhFMdKJZZ3jNvwz6E+cPTEdq2n+7wHNVR0LE1OAsAkxK3jOSllm69v81aGydIlaBhkXBEgciBaJhdW14ZNTcXExDUVnMgxopspNqk33rK+Z84E+OFZa/cSt7mp+MKR5ZUrz0CWKTacU87c9nyeHNJtTQZvUlcrFQyil0BiU6i6+5KZCCCHWsQ2fnDabTY4dO0azeYbelOJdpS8bCTYd+T4vHS1wbHp5M6im71KYmcR1L0ypEKUUpqFQJ82gzjSDdqmb03DnJoNkSDHT1Dw/rvn6YZ+/3+fz/96CzWm4tsugZAcdsVbCNBTXbs0CoIFj0/Xz+KkuTjIuCJA4EC0SC6tL1pwCnie1Hy8msbDJVZtSvD4cFPUfmqnTmYrMl6U6HdNQpC2fqbqmJ3X+zscAajWPifEyhZk68UyMWFuSgw2T6XrQujQZCnbuf/u4JhNWXNlhcG2X5ulRTToUrIkF2JZR7JvRXNW5snWn0ZBJPGJRb9o0HP/8fbiLmIwLAiQORIvEwuqR5FRclKIhk2s2p3nqrTy+rxktNNjelTjr6yImK0pOvbP0uzeAqYkaX/n6furNYNe8ZYBpmdx7/y5u2R5UFBiuavbl4d5ezfR4iWOv1pnOxtjamWSwvfVtujsL3z0BV/gaa4VloTLxEPWmg+1KciqEEGL9kuRUXLSUUsTDBlV7+bfBTSMoJ+prjVKQb2iGKkGXqRt6DYIb44qKo/nxqKbqBs+9bUCRXaLbU7Pu8Z1HD2B4HqYCVwf1TEO+x9PfOcA9H9vDj2eC174nXOfhf9zPdNUjakLNhfakyf337aa9K4ZPMLvbn9CM1TQDyZUlp9l4iNG8olB3ma7YtCfDK3q9EEIIsRokORUXtUTEomo7y27bGVLwZh72lTURE1JWUAhfAwf2+UQtSIY0TS+oX7ojq8g3g+eeSinF+FgZ2/YwVDArq3zwNXhaka95jI2W+bltWUKOx//7h/0Uax4JC8oOJEOgHZ/HH93Pxz65h1AseJNtaXhpCtqjmpi1/AQ1HbVmU2vFeEmSUyGEEOvThk9OQ6EQvb29hELnv3yQWHttiRATZZtq8+zJqWla7NnUxokTFjuzcGX78vYL5paYMQUwFORngs1HGmh6EDcBrXEcFzxNdaZCfFuW4bEyru2RCgWJadyC8Oxte9v2GB8rM7gth9aaZNhgIKl5cUJzY9/yklPb9dk3UsZAYyhNX1bKoZyJjAsCJA5Ei8TC6trwyalhGMRisbM/UbwrpWNBiDcdj0LNYbRpMVTRdMYUIQPilsZSCtuHigMNL4yH5mwb2otNTckJbr03HU3JVdzYCyf3GPU15NpiaDR1F+KGxrV9isUG/mytUe3DD79/iGwuSshQlB29IDGdU8jX2axyzG3Ub4vAs2PwxYM+/QnFlhQMpoJZ0ZNN11x+NlQliovWGlNprhxIkY3LAHsmMi4IkDgQLRILq2vDl5JyXZeZmRlcd/n1MMW7R+ikbkrVpsvunOKaTkUmHMxOjlRhqKqxfQgpj3bKXJnzlljPqfG1RqN5dVrz1KhmtBpsnjpagZ4YcEpiqLWmuydFJGyRsMB1Fyam0ahFPBHi7YNTGIbC9jSGWpyYAmRzsfn6pL7W/HRcM5hU7MjAlhSM1jTfPa55q6Dnz/Wnww3+7vkCxWoTtEfMgl09CXKSmJ6VjAsCJA5Ei8TC6trwM6ee51EoFEgkEljWhr8cFx3LmJtL1PNtPNuiirZo8PjWdCsRdF3N9HSNkVqEmtZMNiBqwmRdM1yFrhgoBZ1Rxb2b5zo+KYKUdWmRmMld9+3imw+/QcP1W4lpxOJDd+/kuWePM1NoEI2HCRmziekpBwuHTbp7Uni+zzNjMFzRXNamyIY0PxqD3TnYlDJwfc33hzRPHm0wNF0lFdLc0AUdCYtLexJnLaUlWmRcECBxIFokFlaXXGFxUVNK0ZOJMFpsMllqko2H6MlEFz1Poyk2NW/OaGYMCIVhUwocL6gxmjnNulIIbqWf7lEf6OyO88CDlzM6UmJkuEwmGyUeD/H8s8eZmqhiGvDKS8N86O6dPPmDQ9hNb34pgBU2ue3OnbxYNNh3VHNNp+LGHYqRKjwzCp0xGK/DtlCQiO9NN0nUa1za7xOzDHqzETa1xc5Y7koIIYRYTyQ5FRe9ze1RSnWHqu1zYqY+n5yO1zUH8nC0pLEMaNqaTQY8sE3x+FAwa9qfeOdJnaehVnfwgbHREocOTVEuBV1G5ppF5fN1lKX46Cf2cOh4mXy+TndHjPauFK9VDIZKmvu3KDpiCtD8aCQoXzVTaTIyZWPYFp6vGc7XMfDZ0h5jW0dsQTcqIYQQ4t1AklNx0YuGTNqSYaozDRq2h+v5GIbicBEuywUboZIhxa60QleD9qLXdiu+c1xz3xZIh99Zgqe1prMzQbPuks/XaTZPWrOkQStFNBGGeJxHhhU3D2aJ9uUYrfrsm9Hc0A039wXLw11fU2263L/ZYCRfY7zQQOEzNB2co4EmGTHZKompEEKId6kNn5yapkkqlcI0ZT3exSxfddAYhEMmlhkkejf0wPMTmvao4vpuhe+bVPwYhmHQG1dsTsGhoubqFbYKXYoZNejoinP3vbv49qP7aTRcfA2GoejqTnD//btpz5l8PAfPjWuipubyHGQiCsfVFGou+arNaLGJ57U6PCl8ejNBvVKtIRm16MtGZtfDinMl44IAiQPRIrGwujZ8cmpZFp2dnWt9GuICS0ZMKk0b2wHX8+cT1CvaYLbaFIZhkk5n5l9zVafmieOwK7eyYvcnOzxZZbTQwPc1yajF5t4kv/gr72FyokK51KS9M0GmI44XUhycqBK2DAYjMFmyeSPvYiiFO5uMKoKd+FFL4fqaiGWwrTNJhxTTP+9kXBAgcSBaJBZW14ZPTn3fx3VdLMvCMDZ8Za2L1qU9CcZLNhoYKTTY1B70tD856dRa43kepmmilOJQUVHzNM+NaW4ZOLfkdKZio30PBVQbNm82bKIhk2RbmGRbhOmGw/BECT27pSrY+69QBAmpR1CgKhoySEYsejIR2hMhuWV/gcm4IEDiQLRILKyuDZ+cOo7D8PAw/f39RCLSNediZShFRzLEZMXh6FSNgbbYolvfnucyPT3NpMox3AhhGXB1p+KN6dMVijq7nmyUIxMeCh9DwXu3ZGi6PvmaQzRkko4qynWXmu3Nz4YaStGWjNCWCKE1xMOm7LZfZTIuCJA4EC0SC6trwyenYuO4pDvBZKWAAqYrNp2ppQeYsRrkYnBdt2K4Aql3ULO+Lxul0nCZKDVB+7w+XOayviRbO+LnflAhhBDiIiZz02LDiFgGqaiJQjNTsU/7vFsGDK7rNgBFZ1xTduArh/zTPn/OdENTdRfOshpKsas3xab2OBqDqu3zwtESrn/us7FCCCHExUySU7GhtCdCgG4VGD2LsGHw0e0GcRO+caTV4elUVVfz6DE9v7nqVJvaY7Qlw2gMNHB8un5uH0AIIYS4yElyCrK5ZAMxlli7afuahjeXdCp8rbF9zUhV8+hRn28d9Rmvw3QDDhWXPu7z43Brv8I4Ta8oQyku60vNJ8UnZho43tlnY8XakXFBgMSBaJFYWD0bfs1pJBJh69ata30aYpXMTXyePMQcKcGPRzXtMRPP68SrgO1pLs0qrutWZCIQNuCpEc1YDXZkFx6z2NTkG5r+vjMPXEopNrfHODZVQymfpusTMuX3w/VIxgUBEgeiRWJhdW345FRsLNWmy0i+iWm1Qn9nVtEWgSeGNO/rhoGkwlpihrUjqhmvaU5ObQ8WNM9PaLIRONuG+nzV5uhUDQOfTNQiGZFvPyGEEOJUG37axrZthoaGsO3Tb5ARF4e64/HC0RLlpod70sal8XrQq/6uAY+UMwO+t+Trd+UUDQ++dNCnbPt8/4TPWwXNhwbhns3BBqozydccjNmKpgNt0fP4ycT5JuOCAIkD0SKxsLo2/NSN1hrbttGn2egiLh4TJZtIyKDu+GgVJKWvTmlqLnxosyICTLsusHQsGEpx92bFgXyw+WlPO+xuU6izJKVz+rJRhmeCjVBvjVVp2x6SNqPrlIwLAiQORIvEwura8Mmp2DhiYQPb9UFB2Axu5V/doeiIAShcd3nH2ZlTvDihmWmw7MQUIBoymVsU4Pl6Ba8UQgghNo4Nf1tfbBzZWIhYyEChcX1NyFB0xBRnux1/qufHfdpj4PqctrTUmWlSMUt2fgohhBBLkORUbBhhy+CqTWm2dMToSa+8/Zzja34w5ONquLlP4QGvT2v80ywDONXQTH0+DU7JZighhBBiSRs+ObUsi66uLixLkoWNwDIU4dOUbzIMk0wmi2GYix5zfM23j2kyEcUNPYqYpbixVzHTgOfGguRUo6l7mmMlzfHywoS12nQ5OlVD4ZONWWzrjJ3/DyfOGxkXBEgciBaJhdW14a+yaZokk8m1Pg2xCrTWjBabADTdxQXwDcMgGl28i77qah4/ptmRVVzZDnPLACKm4ppu+PYxzf59PpYBPTGoOHBDb+uWva81b46U0VpjAFcOpOSW/jon44IAiQPRIrGwulacnO7fv58///M/5/XXXycSiXDdddfxh3/4h3R2dvLYY4/xP//n/2RoaIhcLscDDzzAv/pX/wrDWL8TtK7rUq1WSSQS8hvRRW602KTpajQG05XF5UB836PRaBKNRjAME9vX/HQ8qGV6a79iS3pxQpkKKbpimt4OxY5MsKO/7GieHNb0JeA9nXBipk7D9lD4dKbCmGcriCrWnIwLAiQORIvEwupaUdZo2zaf/vSnue666/jJT37Ct771Laanp/nsZz/LgQMH+MxnPsO//tf/mhdeeIH/9b/+F1/96lf54he/eKHO/bzwPI/p6Wk8b+naluLiMZRvAEGhqN7s4hlS3/cpl0v4vk++qfnSQU1/An5tt7FkYgowWtVMN2D7bGIKQcJ63xaDrWkAxXC+EdzOj1vs6k1coE8nzicZFwRIHIgWiYXVtaL0v16v8wd/8Ac8+OCDWJZFW1sbd9xxB1/4whfYt28fmUyGW2+9FYBt27Zx7bXX8uabby77+Fprms3mgq8ZhkEoFJqvMXaqSCTY2LJU/THLsjBNE8/zcE+pE3TycR3HWfS+4XAYpRSO4+D7C28Bm6aJZVlLHlcpRTgcBlh0TIBQKIRhGLiuuyjI547r+z6O45z2s16o457pGi513LP925zpGp7p3+Zs13A5/zanflbb9SnVbEwrzLauBJ0Jg+dHbRRBO1LLgPGqZqigGZlxSIZ97htQpCNBiSnDMDAME601nhecr+NpXh7XNG3FZC1Eb4LZzxJcw5QJ0yUHx/UwFWjfwznlOl2oa/hOjrvW8b1UHJ7puBdijGg2m/N/fzdeQxkjVj5GLHXcZrO54OfDxXQNZYxY3nFPvoan5grv5mu4VmPEcq0oOc1kMjz00EPzfz98+DAPP/wwd999N9dddx2NRoPHHnuM22+/nWPHjvHCCy/wR3/0R8s+vuu6DA8PL/haMpmkq6tryccgSIIBJicnF13szs5OUqkUlUqF6enpBY/FYjF6e3vRWs8/FgqF5h/fvHkzpmkyPT1NrVZb8Nr29nYymQz1ep2JiYkFj4XDYQYGBgAYGRlZ9I80MDBAOBwmn89TLpcXPJbNZmlra6PZbDI6OrrgMdM02bx5MwBjY2OLAqe3t5dYLEapVKJQKCx4LJVK0dnZueQ1VErN9wuemJhY9A3W1dVFMpmkWq0uuobxeJyenh5831/y32bLli0opZiamqJery94bO4a1mo1JicnFzwWiUTo7+8HWPK4g4ODhEIh8vk8lUplwWNz17DRaDA2Njb/9VLDp1j0aWvvIhmxKBbyRBo+h4qat0cgHoLObJp0CHalG2jPxqnA9Ozh47E4qXQaz3OZnp7G9+H1GU1HBPZkFa/PdOH4EG8W8DwXrTX7xuqUGx6JRAIzbGE36gwP5xecbyKRoLu7G8/zlvysc/82k5OTNBqNBY91dHSQTqepVqtMTU0teCwajdLX13faa7hp0yYsy2JmZoZqtbrgsVwuRy6Xo9FoMD4+vuCxUCjE4OAgsHR89/f3E4lEKBQKlEqlBY9lMhna29uxbZuRkZEFjxmGwZYtWwAYHx9fNPj19PQQj8eXjO8LMUY4jjP//am1XvK4MkYELpYxAhbG9+joKM1mc8HPh76+PqLRKMVikWKxuOC16XSajo4OHMc54zVcKr67u7tJJBKUy2XyeRkj1uMYcXJ8z+UKJ+cRMkYsb4xYLqXPod3B8PAwd955J67r8vGPf5w//uM/xjRNfvCDH/AHf/AH84PMb/zGb/CZz3xm2cf9z//5P/OpT31qwdcu9Mxpo9Hg2LFj9Pb2zh8L5DeeORfLrIivNS+fqND0TXwUVw8miYVNphuakIJ0ROH7mnx+hmw2i2ku3LF/8syp7Tg8MaTZnlFszwS38pVp8c9DGku77MxCexR+/HYefA/LNOhvi9GfCWEpvei4MisSWOtZkVNnTsfGxti8eTPhcPhddw1ljDh/M6ejo6PzPx8upmsoY8Tyjjt3DWu1GidOnFiQK7ybr+FajRFf+tKX+N3f/d1Frz/VOSWnEMwmHDt2jD/6oz+is7OT3/md3+ETn/gEn/vc57j11ls5evQov//7v8+nPvUpfuVXfmVZx/zrv/5rfu3Xfu1cTuecOY7D9PQ07e3tC2ZOxcVnqmLz+nAFjcHu/jQdyfCCxz3Po1wukUqlFyWnJ3tpUpMIwc7s4nWoUw2fiZJNZ0yxb6SMgWZze4StHfHz/nnEhSPjggCJA9EisXB+fOELX1hWcnrOW86UUmzZsoU/+IM/4JOf/CThcJg9e/Zw9913A7Br1y5+8Rd/kS9/+cvLTk7XQigUoqenZ61PQ6yCVLQV7oWasyg5NU2TbDY3//eqq9k/A1vT0BaFhgfPjmqqLty9ZXFiWmm6HB4p03Q8pvNzBac0EWv9VqsQS5NxQYDEgWiRWFhdK0pOf/KTn/DZz36Wb3/72/Ploeb+n0qlFq0RWWqae73RWuP7PoZhSO3Ji5zvBzcJFJrRfJ1qw2Vnb5JoyKRUdxjK16nUXYzZUk+lpkc8HuOn1RB13yQZVmxOwba0wjil5elYscHBsQoKPftfIB42aD8lCRbrn4wLAiQORIvEwupa0ZTOFVdcQaVS4S//8i+p1+vMzMzwX//rf+Xaa6/ltttu44UXXuD73/8+juNw+PBhvvzlL3PHHXdcqHM/L2zb5tixY++KRFq8M7GwyaXdcaKWQuFTqjv89HCeV04UeeV4kfF8leHRUcq1Og3bJaw0Tr1Owi7R7uaJ1GbITxc4MFpmtNCg4XgcHK/w4tHCbGLqEw0prt+a4ed2tvFzO9t479aszJy+C8m4IEDiQLRILKyuFc2cplIpPv/5z/Onf/qnvO997yMej/O+972PP/uzP6O7u5u/+Iu/4G/+5m/4zGc+Qy6X49577+W3fuu3LtS5C7FifdkovZkIb41XGS3agEGp5gR1SKMGoTi0JUMUm5pgonVutjXguD75isvMbBF/RWs2Nhe3uKwvSeg07VGFEEIIcXYrXnO6c+dO/u///b9LPnbfffdx3333veOTEuJCUkqxsydJNt7k8GQNz4ctHXE644phVaS/Jz6/89DxfDxfk6+5lOou6ZjFZNlmphrsVlRATyZMZypMLh6S2z1CCCHEOyQ9uMSG1Z2O0J1ulQ9bsrSGaRAyoTdj0psJntuTDlNpelimIhY6/a5+IYQQQqycJKdCrJBSasHOfyGEEEKcPxv+J2w4HJ7vUiI2NokFMUdiQYDEgWiRWFhdGz45VUpJsAlAYkG0SCwIkDgQLRILq2vDbyt2HIfR0dEl23GJjUViQcyRWBAgcSBaJBZW14ZPTn3fp16vL+p7KzYeiQUxR2JBgMSBaJFYWF0bPjkVQgghhBDrhySnQgghhBBi3ZDkVAghhBBCrBsbPjm1LIv29nYsa8MXLtjwJBbEHIkFARIHokViYXVt+KtsmiaZTGatT0OsAxILYo7EggCJA9EisbC6NvzMqed5lMtlPM9b61MRa0xiQcyRWBAgcSBaJBZW14ZPTl3XZXJyEtd11/pUxBqTWBBzJBYESByIFomF1bXhk1MhhBBCCLF+SHIqhBBCCCHWDUlOhRBCCCHEurHhk1OlFJFIBKXUWp+KWGMSC2KOxIIAiQPRIrGwujZ8KalwOEx/f/9an4ZYByQWxByJBQESB6JFYmF1bfiZUyGEEEIIsX5s+OS02Wxy+PBhms3mWp+KWGMSC2KOxIIAiQPRIrGwujZ8ciqEEEIIIdYPSU6FEEIIIcS6IcmpEEIIIYRYNyQ5FUIIIYQQ64aUkgqHGRwcxLI2/KXY8CQWxByJBQESB6JFYmF1bfirrJQiFAqt9WmIdUBiQcyRWBAgcSBaJBZW14a/re84DhMTEziOs9anItaYxIKYI7EgQOJAtEgsrK4Nn5z6vk+lUsH3/bU+FbHGJBbEHIkFARIHokViYXVt+ORUCCGEEEKsH5KcCiGEEEKIdUOSUyGEEEIIsW6sq936hUKBL3zhC2t9GkIIIYQQ4jybnJxc1vOU1lpf4HMRQgghhBBiWeS2vhBCCCGEWDckORVCCCGEEOuGJKdCCCGEEGLdkORUCCGEEEKsG5KcCiGEEEKIdUOSUyGEEEIIsW5IciqEEEIIIdYNSU6FEEIIIcS6IcmpEEIIIYRYNyQ5FUIIIYQQ68aGS05fe+017rjjDj7+8Y8vekxrzf/+3/+bK664gi9+8YsLHvt3/+7fcdlll3HllVfO/3fttdeu1mmL8+xc4+Bkb7zxBpdddhlf+9rXLuSpigvsXGPBtm3+9E//lBtvvJGrr76aj370ozz55JOrddriPDvXOPB9n//23/4bH/zgB7n66qv5xCc+wQsvvLBapy0ugHfy82Fqaopf//VfZ+fOnTSbzdU43YuStdYnsJq+8Y1v8Fd/9VdccskllEqlRY//5m/+Jlpr0un0kq//7d/+bX7v937vQp+muMDeaRxA8APpj//4j4nH4xfyVMUF9k5i4S//8i959dVX+cpXvkJHRwdf+MIX+L3f+z2eeOIJOjs7V+P0xXnyTuLg//yf/8NXv/pV/u7v/o7Nmzfzt3/7t/zu7/4uTzzxBMlkcjVOX5xH7yQWDhw4wG/+5m9yzTXXrMapXtQ21Mxps9nkH//xH9m7d++Sj1911VX83d/9HdFodJXPTKym8xEHX/ziF0mlUuzevftCnaZYBe8kFt73vvfxZ3/2Z/T09GBZFh/72MdoNpscP378Qp+2OM/eSRwYhsG/+Tf/hh07dhAOh/n0pz9NoVDgrbfeutCnLS6AdxILMzMz/NVf/dWSM65iZTbUzOlDDz10xsd/53d+54yPP/vsszzxxBMcO3aM7du389nPfpYrrrjifJ6iWAXvNA4mJyf57//9v/OFL3yBP/7jPz6fpyZW2TuJhdtuu23+z5VKhb/9279ly5YtXH755eft/MTqeCdx8Ku/+qsL/j42NgZAV1fXOz4vsfreSSzccMMNADz33HPn9Zw2og01c/pODA4Ozt+yefrpp7n22mv59Kc/TT6fX+tTE6vsz//8z3nooYfYtm3bWp+KWAc+/elPc8011/Dkk0/yP/7H/5A7LxuYbdv84R/+IQ888AADAwNrfTpCvGtdVMnpI488ws6dO5f8751uWvnd3/1d/uN//I90d3eTTCb5zGc+Qzgc5vvf//55OntxvlzIOHjmmWd4+eWX+e3f/u3zdLbiQrqQsTDn85//PC+++CL33Xcfv/RLv8T4+Ph5Oa44f1YjDiqVCv/iX/wLTNPkT/7kT87LMcX5txqxIN65i+q2/oc//GE+/OEPr8p7maZJb28vExMTq/J+YvkuVBzYts1/+A//gT/6oz+S2bF3idUaE5LJJP/yX/5LvvrVr/Ktb32LX//1X7/g7ymW70LHwczMDJ/+9KcZGBjgP/2n/yTjwzq2mnmCOHcX1czphaK15s///M/Zv3///Nds2+b48eMMDg6u4ZmJ1fTyyy9z7Ngx/u2//bdcf/31XH/99bz00kt87nOfk5nUDegjH/kITzzxxIKvGYaBZV1Uv/OLs2g2m/zmb/4ml19+Of/lv/wXSUyFOA9kFF0GpRRDQ0P8yZ/8CX/9139NMpnkb/7mbwiFQtx+++1rfXpilVx11VX88Ic/XPC13//93+fuu+/mgQceWJuTEmtm7969/M3f/A07duygt7eXr33ta5w4cYIbb7xxrU9NrKLPf/7zhEIhPve5z2EYMt8jxPmgtNZ6rU9itdx5552MjIzgeR6+7xMKhQB4/PHHGRkZ4dOf/jQQzIpaloVhGLz3ve/l85//PIVCgb/4i7/gqaeeolKpsGfPHj772c+yffv2tfxI4hy8kzg41S//8i/z4IMP8tGPfnRVP4M4P95JLNTrdf7qr/6Kb3zjG9i2zdatW/n93/99fu7nfm4tP5I4B+8kDm6//XZGR0cXJaa//du/fdbKH2L9eSex8O///b/nkUceQWuN4ziEw2EAPve5z/GRj3xkrT7Su9KGSk6FEEIIIcT6JvcghBBCCCHEuiHJqRBCCCGEWDckORVCCCGEEOuGJKdCCCGEEGLdkORUCCGEEEKsG5KcCiGEEEKIdUOSUyGEEEIIsW5IciqEEEIIIdYNSU6FEEIIIcS6IcmpEEIIIYRYNyQ5FUIIIYQQ68b/H4pOtyuIVKheAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gages_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 667
        },
        "id": "pKwMOIdObzSO",
        "outputId": "8439c8ed-91df-41cd-8520-7b378b00a8ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id                                               name  \\\n",
              "0   10011200  WEST FORK BEAR RIVER AT WHITNEY DAM - NR OAKLE...   \n",
              "1   10011500            BEAR RIVER NEAR UTAH-WYOMING STATE LINE   \n",
              "2   10015700  SULPHUR CR.AB.RES.BL.LA CHAPELLE CR.NR EVANSTO...   \n",
              "3   10015900       SULPHUR CREEK BL RES. - NEAR EVANSTON - WYO.   \n",
              "4   10016900                        BEAR RIVER AT EVANSTON - WY   \n",
              "..       ...                                                ...   \n",
              "73  10168500           BIG COTTONWOOD CR NR SALT LAKE CITY UTAH   \n",
              "74  10171000    JORDAN RIVER @ 1700 SOUTH @ SALT LAKE CITY - UT   \n",
              "75  10172700                      VERNON CREEK NEAR VERNON - UT   \n",
              "76  10172860                         WARM CREEK NEAR GANDY - UT   \n",
              "77  10172952                   DUNN CREEK NEAR PARK VALLEY - UT   \n",
              "\n",
              "                      River   latitude   longitude  elevation_m    state  \n",
              "0      WEST FORK BEAR RIVER  40.841614 -110.927119         2797     Utah  \n",
              "1                BEAR RIVER  40.965225 -110.853508         2432     Utah  \n",
              "2             SULPHUR CREEK  41.129114 -110.806563         2205  Wyoming  \n",
              "3             SULPHUR CREEK  41.156058 -110.835176         2173  Wyoming  \n",
              "4                BEAR RIVER  41.270224 -110.963793         2057  Wyoming  \n",
              "..                      ...        ...         ...          ...      ...  \n",
              "73  LITTLE COTTONWOOD CREEK  40.618559 -111.781876         1527     Utah  \n",
              "74             JORDAN RIVER  40.733557 -111.923270         1294     Utah  \n",
              "75             VERNON CREEK  39.979391 -112.380230         1898     Utah  \n",
              "76               WARM CREEK  39.459528 -114.023722         1573     Utah  \n",
              "77               DUNN CREEK  41.858530 -113.327219         1910     Utah  \n",
              "\n",
              "[78 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-61110e93-9c1c-4cd7-baa4-280e5ab91a81\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>name</th>\n",
              "      <th>River</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>elevation_m</th>\n",
              "      <th>state</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10011200</td>\n",
              "      <td>WEST FORK BEAR RIVER AT WHITNEY DAM - NR OAKLE...</td>\n",
              "      <td>WEST FORK BEAR RIVER</td>\n",
              "      <td>40.841614</td>\n",
              "      <td>-110.927119</td>\n",
              "      <td>2797</td>\n",
              "      <td>Utah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10011500</td>\n",
              "      <td>BEAR RIVER NEAR UTAH-WYOMING STATE LINE</td>\n",
              "      <td>BEAR RIVER</td>\n",
              "      <td>40.965225</td>\n",
              "      <td>-110.853508</td>\n",
              "      <td>2432</td>\n",
              "      <td>Utah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10015700</td>\n",
              "      <td>SULPHUR CR.AB.RES.BL.LA CHAPELLE CR.NR EVANSTO...</td>\n",
              "      <td>SULPHUR CREEK</td>\n",
              "      <td>41.129114</td>\n",
              "      <td>-110.806563</td>\n",
              "      <td>2205</td>\n",
              "      <td>Wyoming</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10015900</td>\n",
              "      <td>SULPHUR CREEK BL RES. - NEAR EVANSTON - WYO.</td>\n",
              "      <td>SULPHUR CREEK</td>\n",
              "      <td>41.156058</td>\n",
              "      <td>-110.835176</td>\n",
              "      <td>2173</td>\n",
              "      <td>Wyoming</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10016900</td>\n",
              "      <td>BEAR RIVER AT EVANSTON - WY</td>\n",
              "      <td>BEAR RIVER</td>\n",
              "      <td>41.270224</td>\n",
              "      <td>-110.963793</td>\n",
              "      <td>2057</td>\n",
              "      <td>Wyoming</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>10168500</td>\n",
              "      <td>BIG COTTONWOOD CR NR SALT LAKE CITY UTAH</td>\n",
              "      <td>LITTLE COTTONWOOD CREEK</td>\n",
              "      <td>40.618559</td>\n",
              "      <td>-111.781876</td>\n",
              "      <td>1527</td>\n",
              "      <td>Utah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>10171000</td>\n",
              "      <td>JORDAN RIVER @ 1700 SOUTH @ SALT LAKE CITY - UT</td>\n",
              "      <td>JORDAN RIVER</td>\n",
              "      <td>40.733557</td>\n",
              "      <td>-111.923270</td>\n",
              "      <td>1294</td>\n",
              "      <td>Utah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>10172700</td>\n",
              "      <td>VERNON CREEK NEAR VERNON - UT</td>\n",
              "      <td>VERNON CREEK</td>\n",
              "      <td>39.979391</td>\n",
              "      <td>-112.380230</td>\n",
              "      <td>1898</td>\n",
              "      <td>Utah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>10172860</td>\n",
              "      <td>WARM CREEK NEAR GANDY - UT</td>\n",
              "      <td>WARM CREEK</td>\n",
              "      <td>39.459528</td>\n",
              "      <td>-114.023722</td>\n",
              "      <td>1573</td>\n",
              "      <td>Utah</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>77</th>\n",
              "      <td>10172952</td>\n",
              "      <td>DUNN CREEK NEAR PARK VALLEY - UT</td>\n",
              "      <td>DUNN CREEK</td>\n",
              "      <td>41.858530</td>\n",
              "      <td>-113.327219</td>\n",
              "      <td>1910</td>\n",
              "      <td>Utah</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>78 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('gages_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gages_df",
              "summary": "{\n  \"name\": \"gages_df\",\n  \"rows\": 78,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49997,\n        \"min\": 10011200,\n        \"max\": 10172952,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          10129300,\n          10011200,\n          10129500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 78,\n        \"samples\": [\n          \"WEBER RIVER NEAR PEOA - UTAH\",\n          \"WEST FORK BEAR RIVER AT WHITNEY DAM - NR OAKLEY - UT\",\n          \"WEBER RIVER NEAR WANSHIP - UT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"River\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          \"PROVO RIVER BELOW DEER CREEK\",\n          \"FARMINGTON CREEK\",\n          \"CENTERVILLE CREEK\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7461186516982347,\n        \"min\": 39.459528,\n        \"max\": 42.64492,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          40.75106,\n          40.841614,\n          40.792726\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49708596765338847,\n        \"min\": -114.023722,\n        \"max\": -110.806563,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          -111.370472,\n          -110.927119,\n          -111.404918\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"elevation_m\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 289,\n        \"min\": 1286,\n        \"max\": 2797,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          2057,\n          1709,\n          1876\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Utah\",\n          \"Wyoming\",\n          \"Idaho\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Nearest distance to the reach"
      ],
      "metadata": {
        "id": "1MtEWxujGGyQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd\n",
        "\n",
        "# Read the shapefile\n",
        "stream_gdf = gpd.read_file(\"gsl_stream.shp\")\n",
        "\n",
        "# stream_gdf.head()"
      ],
      "metadata": {
        "id": "lU4UJprFIAx6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stream_gdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "MeDRx3-5cBED",
        "outputId": "986cdd2b-a533-459b-9986-5aa5a187ea4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          fid       LINKNO     DSLINKNO  strmOrder   USContArea   DSContArea  \\\n",
              "0     21771.0  710738369.0  710740353.0        2.0   60097104.0   61334064.0   \n",
              "1     36359.0  710742337.0  710704640.0        3.0   85985648.0   89495320.0   \n",
              "2     43809.0  710706624.0  710678847.0        3.0  132628064.0  132918688.0   \n",
              "3     54280.0  710676862.0  710680830.0        3.0  200862624.0  201842960.0   \n",
              "4     51810.0  710682815.0  710684799.0        3.0  182594144.0  182753904.0   \n",
              "...       ...          ...          ...        ...          ...          ...   \n",
              "6712  19974.0  710533928.0  710535912.0        2.0   40824656.0   41430768.0   \n",
              "6713  29326.0  710535912.0  710641063.0        2.0   48301832.0   53527024.0   \n",
              "6714  19939.0  710337511.0  710690663.0        2.0   11948942.5   23400392.0   \n",
              "6715  19956.0  710686695.0  710688679.0        2.0   29191344.0   33953592.0   \n",
              "6716  29314.0  710688679.0  710690663.0        3.0   51509000.0   51763688.0   \n",
              "\n",
              "      TDXHydroRe  VPUCode  Topologica   LengthGeod   TerminalLi  musk_k  \\\n",
              "0     7020000010    703.0    214807.0  1116.817679  710930643.0  4467.0   \n",
              "1     7020000010    703.0    247818.0  1654.902090  710930643.0  6620.0   \n",
              "2     7020000010    703.0    264750.0   716.739736  710930643.0  2867.0   \n",
              "3     7020000010    703.0    288382.0   709.748489  710930643.0  2839.0   \n",
              "4     7020000010    703.0    282807.0   336.674092  710930643.0  1347.0   \n",
              "...          ...      ...         ...          ...          ...     ...   \n",
              "6712  7020000010    703.0    212007.0   229.206232  710930643.0   917.0   \n",
              "6713  7020000010    703.0    232733.0  2208.638710  710930643.0  8835.0   \n",
              "6714  7020000010    703.0    211948.0   692.266658  710930643.0   793.0   \n",
              "6715  7020000010    703.0    211978.0   395.583057  710930643.0  1582.0   \n",
              "6716  7020000010    703.0    232711.0   402.988550  710930643.0  1612.0   \n",
              "\n",
              "      musk_x                                           geometry  \n",
              "0       0.25  LINESTRING (-113.904 38.00756, -113.90411 38.0...  \n",
              "1       0.25  LINESTRING (-113.89644 38.02733, -113.89633 38...  \n",
              "2       0.25  LINESTRING (-113.89944 38.036, -113.89956 38.0...  \n",
              "3       0.25  LINESTRING (-113.91656 38.06311, -113.91644 38...  \n",
              "4       0.25  LINESTRING (-113.91233 38.05078, -113.91222 38...  \n",
              "...      ...                                                ...  \n",
              "6712    0.25  LINESTRING (-113.53744 39.75856, -113.53733 39...  \n",
              "6713    0.25  LINESTRING (-113.53878 39.774, -113.53878 39.7...  \n",
              "6714    0.25  MULTILINESTRING ((-113.38311 39.774, -113.3832...  \n",
              "6715    0.25  LINESTRING (-113.37789 39.77067, -113.37778 39...  \n",
              "6716    0.25  LINESTRING (-113.37744 39.77389, -113.37756 39...  \n",
              "\n",
              "[6717 rows x 14 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fid</th>\n",
              "      <th>LINKNO</th>\n",
              "      <th>DSLINKNO</th>\n",
              "      <th>strmOrder</th>\n",
              "      <th>USContArea</th>\n",
              "      <th>DSContArea</th>\n",
              "      <th>TDXHydroRe</th>\n",
              "      <th>VPUCode</th>\n",
              "      <th>Topologica</th>\n",
              "      <th>LengthGeod</th>\n",
              "      <th>TerminalLi</th>\n",
              "      <th>musk_k</th>\n",
              "      <th>musk_x</th>\n",
              "      <th>geometry</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21771.0</td>\n",
              "      <td>710738369.0</td>\n",
              "      <td>710740353.0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>1116.817679</td>\n",
              "      <td>710930643.0</td>\n",
              "      <td>4467.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>LINESTRING (-113.904 38.00756, -113.90411 38.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36359.0</td>\n",
              "      <td>710742337.0</td>\n",
              "      <td>710704640.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
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              "      <td>710706624.0</td>\n",
              "      <td>710678847.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>710680830.0</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>LINESTRING (-113.91656 38.06311, -113.91644 38...</td>\n",
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              "    <tr>\n",
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              "      <td>3.0</td>\n",
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              "      <td>182753904.0</td>\n",
              "      <td>7020000010</td>\n",
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              "      <td>336.674092</td>\n",
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              "      <td>LINESTRING (-113.91233 38.05078, -113.91222 38...</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6712</th>\n",
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              "      <td>2.0</td>\n",
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              "      <td>7020000010</td>\n",
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              "    <tr>\n",
              "      <th>6713</th>\n",
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              "      <td>2.0</td>\n",
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              "      <td>2208.638710</td>\n",
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              "      <td>LINESTRING (-113.53878 39.774, -113.53878 39.7...</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>6716</th>\n",
              "      <td>29314.0</td>\n",
              "      <td>710688679.0</td>\n",
              "      <td>710690663.0</td>\n",
              "      <td>3.0</td>\n",
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              "  </tbody>\n",
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            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gages_gdf = gpd.GeoDataFrame(\n",
        "       gages_df,\n",
        "       geometry=gpd.points_from_xy(gages_df.longitude, gages_df.latitude)\n",
        "   )"
      ],
      "metadata": {
        "id": "7xSTbnuGH6ZY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gages_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "dYRWn_BddRUH",
        "outputId": "445f0849-43c9-484b-f8fd-3f129107b014"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "         id                                               name  \\\n",
              "0  10011200  WEST FORK BEAR RIVER AT WHITNEY DAM - NR OAKLE...   \n",
              "1  10011500            BEAR RIVER NEAR UTAH-WYOMING STATE LINE   \n",
              "2  10015700  SULPHUR CR.AB.RES.BL.LA CHAPELLE CR.NR EVANSTO...   \n",
              "3  10015900       SULPHUR CREEK BL RES. - NEAR EVANSTON - WYO.   \n",
              "4  10016900                        BEAR RIVER AT EVANSTON - WY   \n",
              "\n",
              "                  River   latitude   longitude  elevation_m    state  \n",
              "0  WEST FORK BEAR RIVER  40.841614 -110.927119         2797     Utah  \n",
              "1            BEAR RIVER  40.965225 -110.853508         2432     Utah  \n",
              "2         SULPHUR CREEK  41.129114 -110.806563         2205  Wyoming  \n",
              "3         SULPHUR CREEK  41.156058 -110.835176         2173  Wyoming  \n",
              "4            BEAR RIVER  41.270224 -110.963793         2057  Wyoming  "
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "            'suggestCharts', [key], {});\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gages_df",
              "summary": "{\n  \"name\": \"gages_df\",\n  \"rows\": 78,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49997,\n        \"min\": 10011200,\n        \"max\": 10172952,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          10129300,\n          10011200,\n          10129500\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 78,\n        \"samples\": [\n          \"WEBER RIVER NEAR PEOA - UTAH\",\n          \"WEST FORK BEAR RIVER AT WHITNEY DAM - NR OAKLEY - UT\",\n          \"WEBER RIVER NEAR WANSHIP - UT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"River\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          \"PROVO RIVER BELOW DEER CREEK\",\n          \"FARMINGTON CREEK\",\n          \"CENTERVILLE CREEK\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7461186516982347,\n        \"min\": 39.459528,\n        \"max\": 42.64492,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          40.75106,\n          40.841614,\n          40.792726\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49708596765338847,\n        \"min\": -114.023722,\n        \"max\": -110.806563,\n        \"num_unique_values\": 78,\n        \"samples\": [\n          -111.370472,\n          -110.927119,\n          -111.404918\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"elevation_m\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 289,\n        \"min\": 1286,\n        \"max\": 2797,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          2057,\n          1709,\n          1876\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Utah\",\n          \"Wyoming\",\n          \"Idaho\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from shapely.ops import nearest_points\n",
        "from shapely.geometry import Point\n",
        "from tqdm import tqdm\n",
        "\n",
        "def extract_reach_centroids(stream_gdf):\n",
        "    \"\"\"\n",
        "    Extract centroids from stream reaches in the required format\n",
        "\n",
        "    Parameters:\n",
        "    -----------\n",
        "    stream_gdf : GeoDataFrame\n",
        "        Stream network data with geometries\n",
        "\n",
        "    Returns:\n",
        "    --------\n",
        "    DataFrame with reach centroids in decimal degrees\n",
        "    \"\"\"\n",
        "    # Ensure the geometry is in geographic coordinates (WGS84)\n",
        "    if stream_gdf.crs != \"EPSG:4326\":\n",
        "        stream_gdf = stream_gdf.to_crs(\"EPSG:4326\")\n",
        "\n",
        "    # Extract centroids\n",
        "    centroids = []\n",
        "    print(\"Extracting reach centroids...\")\n",
        "    for reach in tqdm(stream_gdf.itertuples(), total=len(stream_gdf)):\n",
        "        centroid = reach.geometry.centroid\n",
        "        centroids.append({\n",
        "            'Reach_ID': reach.LINKNO,\n",
        "            'Latitude': centroid.y,\n",
        "            'Longitude': centroid.x\n",
        "        })\n",
        "\n",
        "    # Convert to DataFrame\n",
        "    centroids_df = pd.DataFrame(centroids)\n",
        "\n",
        "    # Save centroids to CSV\n",
        "    output_file = 'reach_centroids.csv'\n",
        "    centroids_df.to_csv(output_file, index=False)\n",
        "    print(f\"\\nCentroids saved to: {output_file}\")\n",
        "\n",
        "    return centroids_df"
      ],
      "metadata": {
        "id": "rTdg3g9Njosi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "centroids_df = extract_reach_centroids(stream_gdf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DjCw4FXCjpqL",
        "outputId": "7e3c02c7-48f3-4a7f-a3a8-9204b97defe2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting reach centroids...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6717/6717 [00:00<00:00, 36555.83it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Centroids saved to: reach_centroids.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "centroids_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ElPQIYP2j2lV",
        "outputId": "c15fcfae-3257-44ad-b25b-7791e6c2a9f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Reach_ID   Latitude   Longitude\n",
              "0     710738369.0  38.003440 -113.906556\n",
              "1     710742337.0  38.020757 -113.895623\n",
              "2     710706624.0  38.033335 -113.900190\n",
              "3     710676862.0  38.060696 -113.915783\n",
              "4     710682815.0  38.049483 -113.911622\n",
              "...           ...        ...         ...\n",
              "6712  710533928.0  39.757611 -113.537255\n",
              "6713  710535912.0  39.766185 -113.538314\n",
              "6714  710337511.0  39.772597 -113.384340\n",
              "6715  710686695.0  39.769625 -113.376197\n",
              "6716  710688679.0  39.772336 -113.378004\n",
              "\n",
              "[6717 rows x 3 columns]"
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "centroids_df",
              "summary": "{\n  \"name\": \"centroids_df\",\n  \"rows\": 6717,\n  \"fields\": [\n    {\n      \"column\": \"Reach_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 193614.29296502282,\n        \"min\": 710117152.0,\n        \"max\": 711097367.0,\n        \"num_unique_values\": 6717,\n        \"samples\": [\n          710621157.0,\n          710488195.0,\n          710773916.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0142070416425317,\n        \"min\": 37.97809257392286,\n        \"max\": 42.78939265957635,\n        \"num_unique_values\": 6715,\n        \"samples\": [\n          40.45152784437469,\n          41.42298933829293,\n          39.735823771906524\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0401752913240165,\n        \"min\": -114.91968387237172,\n        \"max\": -110.63079296760324,\n        \"num_unique_values\": 6711,\n        \"samples\": [\n          -111.72211896126512,\n          -113.64400730711965,\n          -112.44261386289065\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import geopandas as gpd\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "import geopandas as gpd\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "\n",
        "def process_wells_and_reaches(wells_gdf, stream_gdf, gages_df, centroids_elev_df):\n",
        "    \"\"\"\n",
        "    Process wells and stream reaches with correct UTM distance calculations\n",
        "    \"\"\"\n",
        "    # Convert to UTM projection (EPSG:32612 for Utah area)\n",
        "    print(\"Converting coordinate systems to UTM...\")\n",
        "    wells_utm = wells_gdf.to_crs(\"EPSG:32612\")\n",
        "    stream_utm = stream_gdf.to_crs(\"EPSG:32612\")\n",
        "\n",
        "    print(f\"Coordinate systems after conversion:\")\n",
        "    print(f\"Wells CRS: {wells_utm.crs}\")\n",
        "    print(f\"Streams CRS: {stream_utm.crs}\")\n",
        "\n",
        "    results = []\n",
        "    total_wells = len(wells_utm)\n",
        "\n",
        "    print(\"\\nProcessing wells...\")\n",
        "    for well in tqdm(wells_utm.itertuples(), total=total_wells):\n",
        "        # Calculate distances in meters\n",
        "        distances = stream_utm.geometry.distance(well.geometry)\n",
        "        nearest_reach_idx = distances.idxmin()\n",
        "        nearest_reach = stream_utm.iloc[nearest_reach_idx]\n",
        "        min_distance = distances.min()\n",
        "\n",
        "        # Get reach elevation\n",
        "        reach_elev_data = centroids_elev_df[\n",
        "            centroids_elev_df['Reach_ID'] == nearest_reach['LINKNO']\n",
        "        ]\n",
        "\n",
        "        reach_elevation = reach_elev_data.iloc[0]['Avg_GSE'] if not reach_elev_data.empty else None\n",
        "\n",
        "        # Find downstream gage\n",
        "        downstream_gage = find_downstream_gage(\n",
        "            nearest_reach['LINKNO'],\n",
        "            stream_gdf,\n",
        "            gages_df\n",
        "        )\n",
        "\n",
        "        # Store results\n",
        "        results.append({\n",
        "            'Well_ID': well.Well_ID,\n",
        "            'Reach_ID': nearest_reach['LINKNO'],\n",
        "            'Reach_Elevation': reach_elevation,\n",
        "            'Distance_to_Reach': min_distance,  # Now correctly in meters\n",
        "            'Downstream_Gage': downstream_gage\n",
        "        })\n",
        "\n",
        "    # Convert to DataFrame\n",
        "    results_df = pd.DataFrame(results)\n",
        "\n",
        "    # Print summary statistics\n",
        "    print(\"\\nSummary Statistics:\")\n",
        "    print(f\"Total wells processed: {len(results_df)}\")\n",
        "    print(f\"Wells with downstream gages: {results_df['Downstream_Gage'].notna().sum()}\")\n",
        "    print(f\"Wells with reach elevations: {results_df['Reach_Elevation'].notna().sum()}\")\n",
        "    print(\"\\nDistance statistics (meters):\")\n",
        "    print(f\"  Minimum: {results_df['Distance_to_Reach'].min():.2f} m\")\n",
        "    print(f\"  Maximum: {results_df['Distance_to_Reach'].max():.2f} m\")\n",
        "    print(f\"  Average: {results_df['Distance_to_Reach'].mean():.2f} m\")\n",
        "    print(f\"  Median: {results_df['Distance_to_Reach'].median():.2f} m\")\n",
        "\n",
        "    # Calculate distance distribution\n",
        "    percentiles = [10, 25, 50, 75, 90]\n",
        "    dist_percentiles = np.percentile(results_df['Distance_to_Reach'], percentiles)\n",
        "    print(\"\\nDistance percentiles (meters):\")\n",
        "    for p, v in zip(percentiles, dist_percentiles):\n",
        "        print(f\"  {p}th percentile: {v:.2f} m\")\n",
        "\n",
        "    # Save results\n",
        "    output_file = 'well_reach_relationships_final.csv'\n",
        "    results_df.to_csv(output_file, index=False)\n",
        "    print(f\"\\nResults saved to: {output_file}\")\n",
        "\n",
        "    return results_df\n",
        "\n",
        "# Keep the existing find_downstream_gage function as is\n",
        "\n",
        "def find_downstream_gage(reach_id, stream_gdf, gages_df):\n",
        "    \"\"\"\n",
        "    Find downstream gage using network topology\n",
        "    \"\"\"\n",
        "    current_reach = reach_id\n",
        "    visited_reaches = set()\n",
        "    path_length = 0\n",
        "\n",
        "    while current_reach and path_length < 100:  # Limit path length to 100 reaches\n",
        "        if current_reach in visited_reaches:\n",
        "            break\n",
        "        visited_reaches.add(current_reach)\n",
        "\n",
        "        # Check for gage on current reach\n",
        "        gage_on_reach = gages_df[gages_df['COMID_v2'] == current_reach]\n",
        "        if not gage_on_reach.empty:\n",
        "            return gage_on_reach.iloc[0]['samplingFeatureCode']\n",
        "\n",
        "        # Move downstream\n",
        "        reach_row = stream_gdf[stream_gdf['LINKNO'] == current_reach]\n",
        "        if reach_row.empty:\n",
        "            break\n",
        "\n",
        "        downstream_id = reach_row.iloc[0]['DSLINKNO']\n",
        "        if downstream_id == 0 or pd.isna(downstream_id):\n",
        "            break\n",
        "\n",
        "        current_reach = downstream_id\n",
        "        path_length += 1\n",
        "\n",
        "    return None\n",
        "\n"
      ],
      "metadata": {
        "id": "SAG9cpMQNFuS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "centroids_elev_df = pd.read_csv('reach_centroids_with_Elev.csv')\n",
        "\n",
        "# Run analysis\n",
        "results = process_wells_and_reaches(\n",
        "    wells_gdf,\n",
        "    stream_gdf,\n",
        "    gages_df,\n",
        "    centroids_elev_df\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WfH3vGHaNHBr",
        "outputId": "c89adcf3-3c3c-4a68-ecf7-dd205fd5ef5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Converting coordinate systems to UTM...\n",
            "Coordinate systems after conversion:\n",
            "Wells CRS: EPSG:32612\n",
            "Streams CRS: EPSG:32612\n",
            "\n",
            "Processing wells...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 8752/8752 [12:27<00:00, 11.71it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary Statistics:\n",
            "Total wells processed: 8752\n",
            "Wells with downstream gages: 4861\n",
            "Wells with reach elevations: 8752\n",
            "\n",
            "Distance statistics (meters):\n",
            "  Minimum: 0.00 m\n",
            "  Maximum: 5845.15 m\n",
            "  Average: 727.63 m\n",
            "  Median: 567.54 m\n",
            "\n",
            "Distance percentiles (meters):\n",
            "  10th percentile: 83.51 m\n",
            "  25th percentile: 238.05 m\n",
            "  50th percentile: 567.54 m\n",
            "  75th percentile: 1046.09 m\n",
            "  90th percentile: 1574.03 m\n",
            "\n",
            "Results saved to: well_reach_relationships_final.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Uul3wqqHR2Lv",
        "outputId": "39a8ddc5-0533-4665-b539-06d48ebb0919"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Well_ID     Reach_ID  Reach_Elevation  Distance_to_Reach  \\\n",
              "0  381033113480701  710579638.0           2018.0          15.997118   \n",
              "1  381037113474001  710579638.0           2018.0         387.946514   \n",
              "2  381152113442801  710579638.0           2018.0         642.085536   \n",
              "3  381236113485601  710258231.0           2033.5          95.717740   \n",
              "4  382113113435401  710549872.0           1770.0         375.764745   \n",
              "\n",
              "   Downstream_Gage  \n",
              "0              NaN  \n",
              "1              NaN  \n",
              "2              NaN  \n",
              "3              NaN  \n",
              "4              NaN  "
            ],
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